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EARLY ALZHEIMER’S DISEASE DETECTION
SYSTEM USING DECISION TREE ALGORTIHM
NUR FATIN SHAMIMI BINTI MAMAT
BACHELOR OF COMPUTER SCIENCE
(SOFTWARE DEVELOPMENT)
UNIVERSITI SULTAN ZAINAL ABIDIN
2018
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EARLY ALZHEIMER’S DISEASE DETECTION SYSTEM
USING DECISION TREE ALGORTIHM
NUR FATIN SHAMIMI BINTI MAMAT
Bachelor of Computer Science (Software Development)
Faculty of Informatics and Computing
Universiti Sultan Zainal Abidin, Terengganu, Malaysia
MEI 2018
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DECLARATION
I hereby declare that this report is based on my original work except for quotations
and citations, which have been duly acknowledged. I also declare that it has not been
previously or concurrently submitted for any other degree at Universiti Sultan Zainal
Abidin or other institutions.
________________________________
Name : Nur Fatin Shamimi Binti Mamat
Date : ..................................................
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CONFIRMATION
This is to confirm that: The research conducted and the writing of this report was under my
supervision.
Name : Pn.Nor Surayati Binti Mohamad Usop
Date : ..................................................
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DEDICATION
In the name of Allah, the most Gracious and the Most Merciful, Alhamdulillah, All praise to
Allah who have guided and give me strength to finish and submit the development system
report in due time and without whose help this study which required untiring effort would
have not been possible to complete with time limits. On this special opportunity given to me,
I would like to express my sincere gratitude to my supervisor, MADAM NOR SURAYATI
BINTI MOHAMAD USOP for her supervision and inspiration throughout my final year
project and because of her careful review, criticism, encouragement and discussion have
greatly distribute in completing this proposed proposal for final project. Sincere thanks to all
my panel DR.SYARILLA IRYANI BINTI AHMAD SAANY, DR.ZAHRATUL AMANI
BINTI ZAKARIA and MADAM FAUZIAH BINTI AB.WAHAB because willing to give an
advise and motivate me in order to finish this final project. Also sincere thanks to my beloved
mentor MADAM MAIZAN BINTI MAT AMIN a caring and loving mentor for my final year
studying at UniSZA. Not forgotten to all my classmates and friends for their kindness and
moral support during my study. Last but not least a very personal words of thanks and special
appreciation to my parents, MAMAT BIN EMBONG and ZAITON BINTI YATIM, also to
my siblings for their understanding , endless love and encouragement.
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ABSTRACT
Alzheimer, an old age disease of people over 65 years causes problems with memory,
thinking and behaviour. This disease progresses very slow and its identification in
early stages is very difficult. The symptoms appear slowly and these gradually will
have worse effects. In its early stages, not only the patients themselves but their loved
ones are generally unable to accept that the patient is suffering from disease. On
average patients live of eight years after identification of Alzheimer, but patients
survive from 4 to 20 years depending on their age and other health conditions. Early
detection of Alzheimer’s and its stages is very important, because as it worsens it has
no cure and patients have very dreadful life before their death. Peoples lacking
knowledge and attention to current issues regarding Alzheimer’s disease make it
difficult to detect their loved one illness earlier. Detecting Alzheimer’s as early as
possible is important as studies show that interventions are performed on Alzheimer’s
patient after the initial examination results in cure or at least slow it down. During the
construction of this system I used Iterative Model to collect information by
reconstructing the steps in the Iterative Model and to locate the disease the system
uses the Decision tree to detect. In addition, this system provides the percentage of
detection of an Alzheimer’s patient, if a person is positively taken to Alzheimer’s
specialist for treatment, and if there is no need to see an Alzheimer’s specialist. The
Decision tree classifies the given data item using the values of its attributes. The
decision tree is initially constructed from a set of pre-classified data. With Early
Alzheimer’s Detection System, it can help family detect their loved one earlier.
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ABSTRAK
Alzheimer, penyakit usia tua yang berusia lebih 65 tahun menyebabkan masalah
dengan ingatan, pemikiran dan tingkah laku. Penyakit ini sangat perlahan dan
pengenalannya pada peringkat awal sangat sukar. Gejala-gejala muncul perlahan-
lahan dan secara beransur-ansur akan membawa kepada kesan yang lebih teruk. Pada
peringkat awal, bukan sahaja pesakit itu sendiri tetapi orang yang mereka sayangi
juga tidak dapat menerima bahawa pesakit itu menderita penyakit Alzheimer. Rata-
rata pesakit hidup lapan tahun selepas mengenal pasti Alzheimer, tetapi pesakit dapat
hidup 4 hingga 20 tahun bergantung kepada usia mereka dan keadaan kesihatan yang
lain. Pengesanan awal Alzheimer dan peringkatnya sangat penting, kerana kerana jika
ia semakin buruk ia tidak dapat disembuhkan dan pesakit mempunyai kehidupan yang
sangat dahsyat sebelum kematian mereka. Ramai orang yang kurang berpengetahuan
dan perhatian terhadap isu-isu semasa mengenai penyakit Alzheimer menjadikannya
sukar untuk mengesan penyakit yang dihidapi oleh orang disekeliling mereka..
Mengesan Alzheimer secara awal adalah amat penting kerana kajian menunjukkan
bahawa intervensi dilakukan pada pesakit Alzheimer selepas keputusan peperiksaan
awal untuk menyembuhkan atau sekurang-kurangnya memperlahankannya adalah
sangat berkesan. Semasa pembinaan sistem ini saya menggunakan Model Iteratif
untuk mengumpulkan maklumat dengan membina semula langkah-langkah dalam
Model Iteratif dan untuk mencari penyakit sistem menggunakan pokok Keputusan
untuk mengesan. Di samping itu, sistem ini memberikan peratusan pengesanan
pesakit Alzheimer, jika seseorang positif dibawa ke pakar Alzheimer untuk rawatan,
dan jika tidak perlu melihat pakar Alzheimer. Pokok Keputusan mengklasifikasikan
item data yang diberikan menggunakan nilai atributnya. Pokok keputusan pada
mulanya dibina dari satu set data pra-dikelaskan. Dengan Sistem Pengesanan Awal
Alzheimer, ia dapat membantu keluarga mengesan orang yang mereka sayangi
sebelum terlambat.
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TABLE OF CONTENTS
1.0 BACKGROUND ........................................................................................................... 11
1.1 PROBLEM STATEMENT ............................................................................................ 12
1,2 OBJECTIVE ................................................................................................................. 12
1.3 SCOPE ........................................................................................................................... 13
1.4 LIMITATION OF WORK ............................................................................................. 14
1.5 EXPECTED OUTCOME ............................................................................................. 14
1.6 PROJECT PLANNING ................................................................................................. 15
1.7 REPORT STRUCTURE ................................................................................................ 16
1.8 CHAPTER SUMMARY ................................................................................................ 16
2.1 INTRODUCTION ......................................................................................................... 17
2.1.1 Alzheimer ................................................................................................................ 17
2.1.2 Research on different system that using Decision Tree ......................................... 20
2.1.3Diagnosis of Breast Cancer using Decision Tree Data Mining Technique ............. 20
2.1.4 Lung Cancer Detection using Decision Tree Algorithm ........................................ 21
2.1.5 Intrusion Detection Systems Using Decision Trees................................................ 22
2.2 Comparison Table Between Same Algorithm And Different System ........................... 23
2.3 DECISION TREE ......................................................................................................... 24
2.4 CONCLUSION .............................................................................................................. 27
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3.1 INTRODUCTION ......................................................................................................... 28
3.2 JUSTIFICATION SELECTION .................................................................................... 28
3.3 METHODOLOGY ........................................................................................................ 29
3.4 SYSTEM REQUIREMENT .......................................................................................... 30
3.4.1 Software Requirement ............................................................................................ 30
3.4.2 Hardware Requirement ........................................................................................... 31
3.5 INTRODUCTION OF SYSTEM MODELLING .......................................................... 32
3.6 FRAMEWORK.............................................................................................................. 32
3.7 CONTEXT DIAGRAM ................................................................................................. 34
3.8 DATA FLOW DIAGRAM ............................................................................................ 35
3.9 DATA FLOW DIAGRAM LEVEL 1 ........................................................................... 39
3.9.1 Manage User For Nurse .......................................................................................... 39
3.9.2 Manage User For Doctor ........................................................................................ 40
3.9.3 Manage User For Patient ........................................................................................ 41
3.9.4 Manage Stage For Nurse ......................................................................................... 42
3.9.5 Manage Questionnaire For Nurse ........................................................................... 43
3.9.6 Manage Result For Patient ...................................................................................... 44
3.9.7 Manage Confirmation For Doctor .......................................................................... 45
3.10 ENTITY RELATIONSHIP DIAGRAM ............................................................... 46
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4.1 INTRODUCTION ......................................................................................................... 47
4.2 IMPLEMENTATION AND OUTPUT ......................................................................... 47
4.2.1 Database Design...................................................................................................... 47
4.2.2 Interface Design ................................................................................................. 53
4.3 SOLUTION COMPLEXCITY ................................................................................. 60
4.3.1 Questionnaire Sample ........................................................................................ 60
5.1 INTRODUCTION ......................................................................................................... 65
5.2 PROJECT CONTRIBUTION ........................................................................................ 65
5.2 PROJECT CONSTRAINTS AND LIMITATION ........................................................ 65
5.3 FUTURE WORKS......................................................................................................... 66
5.4 CONCLUSION .............................................................................................................. 66
5.0 REFERENCES .......................................................................................................... 67
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CHAPTER I
INTRODUCTION
1.0 BACKGROUND
Brain is the central part of the human nervous system and any abnormality caused by
any disease can lead to complete failure of human structural function. Alzheimer, an
old age disease of people over 65 years causes problems with memory, thinking and
behaviour. This disease progresses very slow and its identification in early stages is
very difficult. It is not a specific disease and the patients may have problems with
memory, communication, concentrated attention, reasoning, judgment, focusing, and
visual perception. The symptoms appear slowly and these gradually will have worse
effects. In its early stages, not only the patients themselves but their loved ones are
Generally unable to accept that the patient is suffering from disease. Alzheimer’s
patients forget the recent information and face challenges in simple arithmetic. They
also have problems in speaking and writing, misplace things and have difficulty in
retracing. Their interest in job and social events lessens and their mood becomes
unpredictable. One can observe visible changes in the personality of the patients. On
average patients live of eight years after identification of Alzheimer, but patients
survive from 4 to 20 years depending on their age and other health conditions
It is the only cause of death that cannot be prevented, cured, or even slowed.
Alzheimer’s disease exacts an enormous toll on individuals, families and healthcare
system. It is a serious problem affecting many aspects of our society. Until
Alzheimer’s disease can be prevented or cured, the impact of this disease will only
continue to intensify[3].
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1.1 PROBLEM STATEMENT
Currently there is no treatment available to slow or stop the deterioration of brain cells
in individuals with Alzheimer’s disease. The symptoms appear slowly and these
gradually will have worse effects. Five drugs are currently approved that temporarily
slow symptom progression. Despite current lack of disease-modifying therapies,
detecting Alzheimer’s disease as early as possible is important as studies show that
Alzheimer’s disease can significantly improve quality of life through all disease
stages. The effects of early action make it easier for families to accepting that the
patient is suffering from disease and giving more support for the patient.
1,2 OBJECTIVE
The objectives of this project have been defined as we can know whether the goals of the
system have been achieved. There are the following objectives that determine the success of
this system:
I. To design the system for people or family to identify the appropriate risks for their
loved one who may have Alzheimer’s disease and make early diagnosis.
II. To develop Early Detection Alzheimer’s disease using Decision Tree Algorithm to
check the possibility early detection based on symptoms stated.
III. To test the proposed system is functionality and beneficially to users.
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1.3 SCOPE
The scope of the system basically means everything that will be covered in the research
project and who involves in it. It defines clearly the extent of content that will be covered by
the whole system. The scope of the study has to be defined at a preliminary stage and that is
very important.
User/Patient
I. Register
II. Manage profile.
III. Fill the questionnaire of Alzheimer’s disease.
IV. Check the health status on Alzheimer’s disease.
V. Generate report status of Alzheimer’s disease.
VI. View result Alzheimer’s disease status.
Nurse
I. Register
II. Manage profile.
III. Manage Alzheimer’s symptom.
IV. View Alzheimer’s symptom Report.
Doctor
I. Register
II. Manage profile.
III. View result patients.
IV. Confirmed the stage of the Alzheimer’s disease.
V. Generate report.
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1.4 LIMITATION OF WORK
The system will focus on probability early detection of Alzheimer’s disease based on
symptom. The system does not include diagnosing the patient but the percentage of
Alzheimer’s disease probability results is taken from diagnosing and entering into the
system. This system is developing on web based so that it only can be open using a
web browser not in android or iOS application.
1.5 EXPECTED OUTCOME
This system is expected to be implemented in web-based. In addition, this system can
Detect Early Alzheimer’s Disease Detection using Decision Tree to check the
possibility of early detection based on the stated symptoms. So that the system will
help families identify appropriate risks for their loved one who may have Alzheimer’s
disease and make early diagnosis. Additionally, this system also saves time to make a
preliminary check only through the system does not have to queue up for inspection.
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1.6 PROJECT PLANNING
Table 1 Shows Gantt Chart of schedule and planning for this project proposal.
Task / Month
January Febuary March April
Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Project Title
Discussion and
Briefing
Project Title
Registration
Proposal Writing
(introduction)
Proposal Writing
(literature review-
1)
Proposal Writing
(literature review-
2)
Proposal Progress
Presentation and
Evaluation
Discussion and
Correction of
the Proposal
Proposed Solution
–
Methodology – 1
Proposed Solution
–
Methodology – 2
Proof of Concept
Drafting Report
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1.7 REPORT STRUCTURE
The first chapter of this report is the introduction to the projects which includes introduction,
problem statement, objective, scope, limitation of works and planning for this project. The
overall logic of the system is stated here. The second chapter is literature review. This chapter
provide better understanding based on the explanation of related research done in the related
field. Third chapter describe the methodology used in this system. It discuss project
methodology and requirement of software and hardware that guide the system development,
it deals with project design and modelling which the core part in the development process.
The data flow diagram and the context diagram for this system is shown. Entity relationship
diagram is also included to provide better understanding on database design. Fourth chapter
will explain the function and flow of the system with interfaces provided, and a few tests are
done. In the last chapter which is conclusion, the result has been discussed, concluded and
summarised.
1.8 CHAPTER SUMMARY
This chapter basically deliver the early stages about this project development. It explains
more about the initial project development process.
Proposal
Submit draf report
- supervisor
Report Correction
Seminar
Presentation
Final Report
Submission
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CHAPTER II
LITERATURE REVIEW
2.1 INTRODUCTION
The purpose of this chapter is to present selected literature review, which is very important
for the research. This chapter also describes and explains of the literature review carried out
on the system that will be used as references in developing this system. Previous research or
existing system will also be discussed in this section. Literature review aim to review the
critical points of the current knowledge on a particular topic. Therefore, the purpose of the
literature review is to find, read and analyse the literature or any works or studies related to
this system. It is important to well understand about all information to be considered and
related before developing this system. For this project, some research has been done to
understand about Alzheimer’s disease and technique that had been choosing to implement in
the system.
2.1.1 Alzheimer
Alzheimer's is a type of dementia that causes problems with memory, thinking and
behaviour. Symptoms usually develop slowly and get worse over time, becoming severe
enough to interfere with daily tasks.Alzheimer's is not a normal part of aging. The greatest
known risk factor is increasing age, and the majority of people with Alzheimer's are 65 and
older. But Alzheimer's is not just a disease of old age. Approximately 200,000 Americans
under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset
Alzheimer’s)[3].
Alzheimer's worsens over time .Those with Alzheimer's live an average of eight years
after their symptoms become noticeable to others, but survival can range from four to 20
years, depending on age and other health conditions[3].
Alzheimer's disease typically progresses slowly in three general stages — mild (early-
stage), moderate (middle-stage), and severe (late-stage). Since Alzheimer's affects people in
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different ways, each person will experience symptoms - or progress through Alzheimer's
stages - differently.[4]
The stages below provide an overall idea of how abilities change once symptoms
appear and should only be used as a general guide. They are separated into three different
categories: mild Alzheimer's disease, moderate Alzheimer's disease and severe Alzheimer's
disease. Be aware that it may be difficult to place a person with Alzheimer's in a specific
stage as stages may overlap[4].
2.1.1.1 Mild Alzheimer's disease (early-stage)
In the early stage of Alzheimer's, a person may function independently. He or she may still
drive, work and be part of social activities. Despite this, the person may feel as if he or she is
having memory lapses, such as forgetting familiar words or the location of everyday
objects[5].
Friends, family or others close to the individual begin to notice difficulties. During a detailed
medical interview, doctors may be able to detect problems in memory or concentration.
Common difficulties include[4]:
Problems coming up with the right word or name
Trouble remembering names when introduced to new people
Challenges performing tasks in social or work settings.
Forgetting material that one has just read
Losing or misplacing a valuable object
Increasing trouble with planning or organizing
2.1.1.2 Moderate Alzheimer's disease (middle-stage)
Moderate Alzheimer's is typically the longest stage and can last for many years. As the
disease progresses, the person with Alzheimer's will require a greater level of care[4].
You may notice the person with Alzheimer's confusing words, getting frustrated or angry, or
acting in unexpected ways, such as refusing to bathe. Damage to nerve cells in the brain can
make it difficult to express thoughts and perform routine tasks[4].
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At this point, symptoms will be noticeable to others and may include:
Forgetfulness of events or about one's own personal history
Feeling moody or withdrawn, especially in socially or mentally challenging situations
Being unable to recall their own address or telephone number or the high school or
college from which they graduated
Confusion about where they are or what day it is
The need for help choosing proper clothing for the season or the occasion
Trouble controlling bladder and bowels in some individuals
Changes in sleep patterns, such as sleeping during the day and becoming restless at
night
An increased risk of wandering and becoming lost
Personality and behavioural changes, including suspiciousness and delusions or
compulsive, repetitive behaviour like hand-wringing or tissue shredding
2.1.1.3 Severe Alzheimer's disease (late-stage)
In the final stage of this disease, individuals lose the ability to respond to their environment,
to carry on a conversation and, eventually, to control movement. They may still say words or
phrases, but communicating pain becomes difficult. As memory and cognitive skills continue
to worsen, significant personality changes may take place and individuals need extensive help
with daily activities[4].
At this stage, individuals may:
Need round-the-clock assistance with daily activities and personal care
Lose awareness of recent experiences as well as of their surroundings
Experience changes in physical abilities, including the ability to walk, sit and,
eventually, swallow
Have increasing difficulty communicating
Become vulnerable to infections, especially pneumonia
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2.1.2 Research on different system that using Decision Tree
A few researches on different journal that used Decision Tree are used to compared
which technique that suit with the complexity of the system depends on the problem
statement stated
2.1.3Diagnosis of Breast Cancer using Decision Tree Data Mining Technique
This paper presents a decision tree based data mining technique for early detection of
breast cancer. Breast cancer diagnosis differentiates benign (lacks ability to invade
neighbouring tissue) from malignant (ability to invade neighbouring tissue) breast tumours.
This paper also discusses various data mining approaches that have been utilized for breast
cancer diagnosis, and also summarizes breast cancer in general (types, risk factors, symptoms
and treatment). Data mining techniques tends to simplify the prediction segment. Decision
tree is a classifier that is expressed as a recursive partition of the instance space. It creates a
predictive model, which maps observations about a node to conclusions about the nodes’
target value. Decision tree provides a powerful technique for classification and prediction in
Breast Cancer diagnosis problem. In this paper we have chosen J48 decision tree algorithm to
establish the model.[1]
The tree generated by J48 can be used for classification of whether a patient had
benign or malignant tumour. The data mining technique uses the concept of information
entropy. Each attribute of the data is used to make a decision by splitting the data into smaller
modules. It examines normalized information gain (IG) (difference in entropy) those results
from choosing an attribute as a split point. The highest normalized IG is used at the root of
the tree. The procedure is repeated until the leaf node is created for the tree specifying the
class attribute that is chosen[1].
.
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2.1.4 Lung Cancer Detection using Decision Tree Algorithm
Lung cancer, also known as lung carcinoma a malignant lung tumour characterized by
uncontrolled cell growth in tissues of the lung. If left untreated, this growth can spread
beyond the lung by the process of metastasis into nearby tissue or other parts of the body.
Most cancers that start in the lung, known as primary lung cancers, are carcinomas. The
two main types are small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma
(NSCLC).Cigarette smoking is the principal risk factor for development of lung cancer. A
Few popular technique are used to Detect the lungs cancer like support vector machine.
(SVM), naive bayes classifier. A new approach to detect the lungs cancer by Decision
tree algorithm will provide effective result as compare to other algorithm. The proposed
system will enhance the performance of prediction and classification[2].
A decision tree is a decision support tool that uses a tree-like graph or model of
decisions and their possible consequences, including chance event outcomes, resource
costs, and utility. It is one way to display an algorithm. A decision tree is a flowchart-like
structure in which each internal node represents a "test" on an attribute (e.g. whether a
coin flip comes up heads or tails), each branch represents the outcome of the test and each
leaf node represents a class label (decision taken after computing all attributes). The paths
from root to leaf represent classification rules. The Data comes into the database is of
training data , through which the system is trained[2].
To make proper decision on lung cancer Decision tree algorithm is applied on
available data to get system train and ready to take decision for unknown data.Once
Decision tree algorithm is applied on training data, it generates an tree like structure
based on data available in training database. Splitting and aggregation of data is done
while decision tree is generating[2].
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2.1.5 Intrusion Detection Systems Using Decision Trees
Security of computers and the networks that connect them is increasingly becoming of
great significance. Intrusion detection is a mechanism of providing security to computer
networks. Although there are some existing mechanisms for Intrusion detection, there is need
to improve the performance. Data mining techniques are a new approach for Intrusion
detection. In this paper we investigate and evaluate the decision tree data mining techniques
as an intrusion detection mechanism and we compare it with Support Vector Machines
(SVM). Intrusion detection with Decision trees and SVM were tested with benchmark 1998
The Defence Advanced Research Projects Agency (DARPA) Intrusion Detection data set.
Our research shows that Decision trees gives better overall performance than the SVM.
Decision tree induction is one of the classification algorithms in data mining. The
Classification algorithm is inductively learned to construct a model from the reclassified data
set. Each data item is defined by values of the attributes. Classification may be viewed as
mapping from a set of attributes to a class. The Decision tree classifies the given data item
using the values of its attributes. The decision tree is initially constructed from a set of pre-
classified data. The main approach is to select the attributes, which best divides the data items
into their classes. According to the values of these attributes the data items are partitioned.
This process is recursively applied to each partitioned subset of the data items. The process
terminates when all the data items in current subset belongs to the same class. A node of a
decision tree specifies an attribute by which the data is to be partitioned. Each node has a
number of edges, which are labelled according to a possible value of the attribute in the
parent node. An edge connects either two nodes or a node and a leaf. Leaves are labelled with
a decision value for categorization of the data[3].
Induction of the decision tree uses the training data, which is described in terms of the
attributes. The main problem here is deciding the attribute, which will best partition the data
into various classes. The Iterative Dichotomiser 3 (ID3) algorithm uses the information
theoretic approach to solve this problem. Information theory uses the concept of entropy,
which measures the impurity of a data items. The value of entropy is small when the class
distribution is uneven, that is when all the data items belong to one class. The entropy value
is higher when the class distribution is more even, that is when the data items have more
classes. Information gain is a measure on the utility of each attribute in classifying the data
items. It is measured using the entropy value. Information gain measures the decrease of the
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weighted average impurity (entropy) of the attributes compared with the impurity of the
complete set of data items. Therefore, the attributes with the largest information gain are
considered as the most useful for classifying the data items[3].
2.2 Comparison Table Between Same Algorithm And Different System
Table 2-1: Shows the comparison between same Decision Tree algorithm and different
system.
Author/Journa
l
/Year
Author/Year
System Name Method Description Advantages
Ronak Sumbaly,
N. Vishnusri, S.
Jeyalatha
Department of
Computer
Science BITS,
Pilani – Dubai
United Arab
Emirates..
International
Journal of
Computer
Applications
(0975 – 8887)
Volume 98–
No.10, July
2014
Diagnosis of
Breast Cancer
using Decision
Tree Data
Mining
Technique
Decision
Tree
This paper presents a
decision tree based data
mining technique for
early detection of
breast cancer. Breast
cancer diagnosis
differentiates benign
(lacks ability to invade
neighbouring tissue)
from malignant (ability
to invade neighbouring
tissue) breast tumours.
This paper also
discusses various data
mining approaches that
have been utilized for
breast cancer diagnosis,
and also summarizes
breast cancer in general
(types, risk factors,
symptoms and
treatment).
Experimental
results show the
effectiveness of
the proposed
model and
shows the
accuracy
measures of the
result
Ms. Leena Patil,
Ms. Aparna
Sirsat, Ms.
Diksha Kamble,
Mr.Yogesh
Pawar [12]
Lung Cancer
Detection
using Decision
Tree
Algorithm
Decision
Tree
A new approach to
detect the lungs cancer
by Decision tree
algorithm will provide
effective result as
compare to other
algorithm. The
proposed system will
To make proper
decision on lung
cancer Decision
tree algorithm is
applied on
available data to
get system train
and ready to
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enhance the
performance of
prediction and
classification.
take decision for
unknown data.
Sandhya
Peddabachigari,
Ajith
Abraham*,
Johnson
Thomas
[10]
Intrusion
Detection
Systems Using
Decision Trees
Decision
Tree
To classify an unknown
object, one starts at the
root of the decision tree
and
follows the branch
indicated by the
outcome of each test
until a leaf node is
reached. The
name of the class at the
leaf node is the
resulting classification.
select the
attributes, which
best divides the
data items into
their classes
Table 2-1 : Shows the comparison between the related journals with different System
2.3 DECISION TREE
In a tree structure leaves represent the class labels and branches represent
conjunctions of feature leading to the class labels. Figure 9 shows the illustrated example of
binary decision tree[13].
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Steps of Construct a Decision-Tree
There are few steps for construction of Decision tree:
1. First step is check whether all the cases belong to the same class and if Yes then tree is a
leaf and that node is labelled by that class.
2. Entropy and information gain are calculated for each and every attribute
3. Assume best selection criteria and accordingly consider the splitting attribute.
4. Counting the information gain: The concept of entropy arrives in this part. Entropy can be
stated as its measure of any disordered in the data. Entropy can also be called as a
measurement of uncertainty in any random variable.
5. Pruning: For the tree creation process, pruning is an important technique to be performed.
The dataset may sometimes contain subsets that are not well defined of instances, so for
classification of such a subsets, Pruning can be used [4].
6. Pruning has two types:
i. Post Pruning: This type of Pruning is performed after the creation of tree.
ii. Online Pruning: This type of Pruning is performed during the process of tree
creation.
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5.3 Formulas for Entropy Calculation
Entropy= - p(a)*log(p(a)) – p(b)*log(p(b))
P(a) and P(b) is the probability of class (a) and (b) Compute it as the proportion of
class a&b in the set.
Information Gain=entropy (after)- entropy (before)
Probability of class: [No of instances of particular class/ Total no of instances]
Example->
ends – vowel
[9m,5f]
Notation reprents the class distribution of
/ \
Instances that reached a node
=0 =0
-------- ----------
[3m,4f] [6m,1f]
As you can see, before the split we had 9 males and 5 females, i.e.
P(m)=9/14 and P(f)=5/14. According to the definition of entropy:
Entropy before = -P(f) * log2 p(f) – p(m) log2 p(m)
Entropy before = - (5/14) * log2(5/14) -(9/14) * log2 (9/14) = 0.9403
Next we compare it with the entropy computed after considering the
split by looking at two child branches. In the left branch of ends-vowel=1, we
have:
Entropy left= - (3/7) * log2 (3/7) – (4/7) * log2 (4/7) =0.9852
t = - (6/7) * log2 (6/7) - (1/7) * log2 (1/7)=0.5917
We combine the left/right entropies using the number of instances
down each branch as weight factor (7 instances went left, and 7 instances went
right), and get the final entropy after the split:
Entropy after = 7/14 * entropy left + 7/14* entropy right=0.7885
Now by comparing the entropy before and after the split, we obtain a
measure of information gain, or how much information we gained by doing
the split using that particular feature:
Information Gain = [Entropy before-Entropy after]=0.1518
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2.4 CONCLUSION
This chapter discusses literature review that had been reviewed during feasibility
studies. The literature review helps developer to discover the problem of previous research or
system which needs to be improves and overcome in this system development. Furthermore,
it also helps to gain understanding about the system that undergo the development process.
As a conclusion, Decision Tree Algorithm is the most suitable method to use in
developing the system. Decision Tree Algorithm can store all the rules in „working memory‟
which means all the requirements to get successfully get into hostel placement can be stored
in Decision Tree Algorithm.
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CHAPTER III
METHODOLOGY
3.1 INTRODUCTION
In this chapter, it will focused on the methodology that being applied in the
software development. The methodology of software development is the method in
managing project development. There are many model of the methodology are
available such as Waterfall model,V model, Incremental model, RAD model, Agile
model ,Iterative model and Spiral model . However, it still need to be consider by
developer to decide which is will be used in the project. The methodology model is
useful to manage the project efficiently and able to help developer from getting any
problem during time of development. Also, it help to achieve the objective and scope
of the projects. In order to build the project, it need to understand the stakeholder
requirements.
3.2 JUSTIFICATION SELECTION
For this project, we purpose Iterative Model as the model of the methodology, which has
been widely applied in the other project. It is because of few reasons. Iterative Model is a
particular implementation of a software development life cycle (SDLC) that focuses on an
initial, simplified implementation, which then progressively gains more complexity and a
broader feature set until the final system is complete[10]. There are many advantage of using
Iterative model. Primary advantage of the iterative model is the ability to rapidly adapt to the
ever-changing needs of both the project or the whims of the client. Even fundamental
changes to the underlying code structure or implementations (such as a new database system
or service implementation) can typically be made within a minimal time frame and at a
reasonable cost, because any detrimental changes can be recognized and reverted within a
short time frame back to a previous iteration. The iterative model is best thought of as a
cyclical process. There are five phases that involved in the iterative model that including
Planning & Requirements, Analysis & Design, Implementation, Testing and Evaluation[11].
For each phase, there are activities are involved. In 3.3 section, there is explanation of the
activity of each phase[10].
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Figure 3.1 below shown that the planning phase as the start and evaluation as last phase.
Figure 3.1:Iterative Model
3.3 METHODOLOGY
In the Early Alzheimer’s Disease Detection System, Iterative model has been chosen as the
methodology .There are Five phases that involve in the iterative model[11]:
1) Planning & Requirements:
The first step is go through an initial planning stage to map out the specification documents,
establish software or hardware requirements, and generally prepare for the upcoming stages
of the cycle. The project has been discussed with project supervisor. From that
discussion, Early Alzheimer’s Disease Detection System has been proposed. The requirement
and risk was assessed after doing study on existing system and do literature review about
another existing research[11].
2) Analysis & Design:
Once planning is complete, an analysis is performed to nail down the appropriate business
logic, database models, and the like that will be required at this stage in the project. The
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design stage also occurs here, establishing any technical requirements (languages, data layers,
services, etc) that will be utilized in order to meet the needs of the analysis stage.
3) Implementation[11]:
With the planning and analysis out of the way, the actual implementation and coding process
can now begin. All planning, specification, and design docs up to this point are coded and
implemented into this initial iteration of the project[11].
4) Testing:
Once this current build iteration has been coded and implemented, the next step is to go
through a series of testing procedures to identify and locate any potential bugs or issues that
have have cropped up[11].
5) Evaluation:
Once all prior stages have been completed, it is time for a thorough evaluation of
development up to this stage. This allows the entire team, as well as clients or other outside
parties, to examine where the project is at, where it needs to be, what can or should change,
and so on[11].
3.4 SYSTEM REQUIREMENT
Based on techopedia.com, the implementation that the system needed to make sure the
hardware or software can be run smoothly. If not success in fulling the requirement,
the failure of performance and installation may occur.
3.4.1 Software Requirement
The software requirements are needed to build system are:
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Table 3.1: List of Software
SOFTWARE
DESCRIPTION
PHP Programming Language for writing a coding
of this system
XAMPP Server MySQL
Using this software to create database and
manipulate database and connect database
with PHP.
Edraw Max Create and design Data Flow Diagram
and Context Diagram
Dropbox Save and update the document for this
system and also as the backup file.
Baidu Browser Medium to find reference to do system
and as medium to system be display and
run.
Notepad++ As medium to write PHP coding to build
system.
Microsoft Word Write the documentation
3.4.2 Hardware Requirement
The hardware requirements to build the system are:
Table 3.2: List of Hardware
HARDWARE
DESCRIPTION
1) Laptop LENOVO G40
Intel(R) Core(TM) i5-5200U CPU @ 2.20GHz
2.20GHz
4.00GB RAM
Window 8 operating system
64 bit Operating system,
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3.5 INTRODUCTION OF SYSTEM MODELLING
By Kast and Rosenzweig, system is organized and complicated one. So, system modelling
able to assist analyst be capable in understanding functionality and models of their system to
present the system to stakeholders. Systems are presented in different models which are
created from different perspectives. There are three perceptive such as external, behavior and
structural. Examples of model are Framework, context diagram, Data Flow Diagrams (DFD)
and Entity Relationship Diagram (ERD). DFD are modelling the system from functional
aspects. It also can show the flow of data between systems. While Entity Relationship
Diagram are used to describe the relationship between entities and attributes of entities. It
widely available in database modelling. Next, another explanation will be available in this
chapter.
3.6 FRAMEWORK
Framework is basic structures that are needed to solve the complex problem or as known as
the tools and material or component. In the Early Alzheimer’s Disease Detection System,
there are three users that we called it as Doctor, Nurse and Patient.
For Doctor, they need log into the system if they want manage their system. After
login, they are retrieved into their own interface (different interface with user interface)
.They can manage profile, manage patient/user’s profile, confirmed the stage of the
Alzheimer’s disease and generate report.
For Nurse, they need log into the system if they want manage their system. After
login, they are retrieved into their own interface (different interface with user interface)
.They can manage profile, manage patient/user’s profile, add, delete and update Alzheimer’s
symptom.
While for Patient, they need register firstly to gain PatientID , email and password. The
PatientID, user Name and password will be used by them to log into the system. After
successfully login, they can use Early Alzheimer’s Disease Detection System by answer the
questionnaire that given. With the answer, the system will generate the result about the user’s
potential to get Alzheimer and they will advise to seek doctors’ confirmation to find out real
results. They also can view information about Alzheimer’s disease.
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Figure 3.2:Framework for Diabetes Prediction System.
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3.7 CONTEXT DIAGRAM
Figure 3.3 show the Context Diagram for Early Alzheimer’s Disease Detection System.
There are three actor are involved in this system which is Patient, Doctor and Nurse. In
context diagram, the flow of the actors are explained and their ability in this system.
Figure 3.3 :Context Diagram
Description of Context Diagram
Based on figure 3.3, the ALZHEIMER’S DISEASE DETECTION SYSTEM process at the
centre of figure. There are three entities or actors are available are PATIENT, DOCTOR and
NURSE. There are twelve data flows in the Context Diagram. Only two outgoing data flow
from DOCTOR which consist of DOCTOR’S DETAIL and ALZHEIMER’S DISEASE
CONFIRMATION STATUS. While from PATIENT, there also two outgoing data flow
which consist of PATIENT’S DETAILS and QUESTIONNAIRE DETAIL. For NURSE,
there are also three outgoing data flow which consist of NURSE’S DETAIL ,
QUESTIONNAIRE DETAILS AND ALZHEIMER STAGE DETAILS. For ingoing data
flow, DOCTOR have only one ingoing data which is QUESTIONNAIRE INFORMATION.
For ingoing data flow, DOCTOR have only have two which is QUESTIONNAIRE
INFORMATION and REPORT STATUS. PATIENT also has two ingoing data flow which is
ALZHEIMER’S DISEASE CONFIRMATION STATUS and REPORT STATUS.
0
Early Alzheimer's
Disease Detection
System Using Decision
Tree Algorithm
PATIENT
Patient's Detail
Questionnaire Detail
Alzheirmer's Disease Confirmation Status
Report StatusDOCTOR
Report Status
Questionnaire Information
Doctor's Detail
Alzheimer's Disease Confirmation Status
NURSE
Nurs
e's
Deta
ils
Alz
heim
er S
tage D
eta
ils
Questio
nnaire
Deta
ils
Questio
nnaire
Info
rmatio
n
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3.8 DATA FLOW DIAGRAM
Figure 3.4 show the Data Flow Diagram level 0 for the Early Alzheimer’s Disease Detection
System. Since the figure 3.4 has been explained the flow of the actors; Patient, Doctor and
Nurse, in this chapter, the more details about the flow are explained with DFD
LEVEL 0 and following by DFD LEVEL 1. The functionality for each process also
will be described and able to help developer to understand their system.
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Figure 3.4 Data Flow Diagram Level 0 Early Alzheimer’s Disease Detection System
1.0
Register
2.0
Manage User
3.0
manage
Alzheimer
Stage
4.0
Manage
Questionnaire
5.0
Manage Result
6.0
Manage
Alzheimer
confirmation
7.0
Report
Nurse
Doctor
Patient
D1 Nurse
D2 Doctor
D3 Patient
D4 Stage
D5 Questionnaire
D6 Result
Nurse Detail Nurse Information
Nurse Detail
Nurse Information
Alzheimer Stage Details Alzheimer Stage Information
Alzheimer Stage Information
Questionnaire Details
Questionnaire
Information
Doctor InformationDoctor Detail
Doctor Detail Doctor Information
Alzheimer Confirmation Alzheimer Confirmation Status
Alzheimer Confirmation Status
Answering Details Questionnaire result
Questionnaire result
Patient Detail Patient Information
Patient Detail Patient Information
Alzheimer Confirmation Status
Questionnaire Information
Report Status
Report Status
Report Information
Report Status
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Description of Data Flow Diagram level 0
There are three entities which are PATIENT, NURSE and DOCTOR. While there are
seven processes are identified such as REGISTRATION, MANAGE USER, MANAGE
STAGE, MANAGE QUESTIONNAIRE, MANAGE RESULT, MANAGE
CONFIRMANTION and lastly, REPORT. Next, NURSE, DOCTOR, PATIENT, STAGE,
QUESTIONNAIR and RESULT are the seven data stores for Early Alzheimer’s Disease
Detection System.
1) NURSE input NURSE DETAILS into REGISTRATION which output is
NURSE INFORMATION into NURSE data store.
2) NURSE input NURSE DETAIL into MANAGE USER which output is
NURSE INFORMATION into NURSE data store.
3) NURSE input ALZHEIMER’S STAGE DETAIL into MANAGE
ALZHEIMER STAGE which output is ALZHEIMER’S STAGE
INFORMATION into STAGE data store and invoke ALZHEIMER’S STAGE
INFORMATION input into MANAGE QUESTIONNAIRE which output
QUESTIONNAIRE INFORMATION to QUESTIONNAIRE data store
4) NURSE input ALZHEIMER’S STAGE DETAIL into MANAGE
ALZHEIMER STAGE which output is ALZHEIMER’S STAGE
INFORMATION into STAGE data store and invoke ALZHEIMER’S STAGE
INFORMATION input into MANAGE QUESTIONNAIRE which output
QUESTIONNAIRE INFORMATION to QUESTIONNAIRE data store
5) NURSE input QUESTIONNAIRE DETAIL into MANAGE
QUESTIONNAIRE which output is QUESTIONNAIRE INFORMATION
into QUESTIONNAIRE data store and invoke QUESTIONNAIRE
INFORMATION input into MANAGE RESULT which output
QUESTIONNAIRE RESULT to RESULT data store
6) DOCTOR input DOCTOR DETAILS into REGISTRATION which output is
DOCTOR INFORMATION into DOCTOR data store.
7) DOCTOR input DOCTOR DETAIL into MANAGE USER which output is
DOCTOR INFORMATION into DOCTOR data store.
8) DOCTOR input ALZHEIMER CONFIRMATIONN into MANAGE
ALZHEIMER CONFIRMATION which output is ALZHEIMER
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CONFIRMATION STATUS into RESULT data store and invoke
ALZHEIMER CONFIRMATION STATUS into PATIENT.
9) PATIENT input PATIENT DETAILS into REGISTRATION which output is
PATIENT INFORMATION into PATIENT data store.
10) PATIENT input PATIENT DETAIL into MANAGE USER which output is
PATIENT INFORMATION into PATIENT data store.
11) PATIENT input QUESTIONNAIRE DETAILS into MANAGE RESULT
which output is QUESTIONNAIRE RESULT into RESULT data store and
invoke QUESTIONNAIRE RESULT input into MANAGE ALZHEIMER
CONFIRMATION which output ALZHEIMER CONFIRMATION STATUS
to RESULT data store and invoke ALZHEIMER CONFIRMATION STATUS
input into PATIENT.
12) All entities and data stores will input the REPORT into REPORT which is
output is REPORT
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3.9 DATA FLOW DIAGRAM LEVEL 1
3.9.1 Manage User For Nurse
Figure 3.5: Data Flow Diagram Level 1 for Manage User For Nurse
Description :
1. An NURSE input NURSE DETAIL to LOGIN process and then the process send
NURSE INFORMATION into NURSE data store.
2. An NURSE input NURSE DETAIL to ADD USER DETAIL process and then the
process send NURSE INFORMATION into NURSE data store.
3. An NURSE input NURSE DETAIL to UPDATE USER DETAIL process and then
the process send NURSE INFORMATION into NURSE data store.
4. An NURSE input NURSE DETAIL to DELETE USER DETAIL process and then the
process send NURSE INFORMATION into NURSE data store.
2.1
Login
2.2
Add User
Detail
2.3
Update User
Detail
2.4
Delete User
Detail
NurseD1 Nurse
Nurse Details
Nurse Details
Nurse Details
Nurse Details
Nurse Information
Nurse Information
Nurse Information
Nurse Information
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3.9.2 Manage User For Doctor
Figure 3.6: Data Flow Diagram Level 1 for Manage User For Doctor
Description :
1. An DOCTOR input DOCTOR DETAIL to LOGIN process and then the process send
DOCTOR INFORMATION into DOCTOR data store.
2. An DOCTOR input DOCTOR DETAIL to ADD USER DETAIL process and then the
process send DOCTOR INFORMATION into DOCTOR data store.
3. An DOCTOR input DOCTOR DETAIL to UPDATE USER DETAIL process and
then the process send DOCTOR INFORMATION into DOCTOR data store.
4. An DOCTOR input DOCTOR DETAIL to DELETE USER DETAIL process and
then the process send DOCTOR INFORMATION into DOCTOR data store.
2.1
Login
2.2
Add User
Detail
2.3
Update User
Detail
2.4
Delete User
Detail
DoctorD2 Doctor
Doctor Details
Doctor Details
Doctor Details
Doctor Details
Doctor Information
Doctor Information
Doctor Information
Doctor Information
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3.9.3 Manage User For Patient
Figure 3.7: Data Flow Diagram Level 1 for Manage User For Patient
Description :
1. An PATIENT input PATIENT DETAIL to LOGIN process and then the process send
PATIENT INFORMATION into PATIENT data store.
2. An PATIENT input PATIENT DETAIL to ADD USER DETAIL process and then
the process send PATIENT INFORMATION into PATIENT data store.
3. An PATIENT input PATIENT DETAIL to UPDATE USER DETAIL process and
then the process send PATIENT INFORMATION into PATIENT data store.
4. An PATIENT input PATIENT DETAIL to DELETE USER DETAIL process and
then the process send PATIENT INFORMATION into PATIENT data store.
2.1
Login
2.2
Add User
Detail
2.3
Update User
Detail
2.4
Delete User
Detail
PatientD3 Patient
Patient Details
Patient Details
Patient Details
Patient Details
Patient Information
Patient Information
Patient Information
Patient Information
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3.9.4 Manage Stage For Nurse
Figure 3.8: Data Flow Diagram Level 1 for Manage Stage For Nurse
Description :
1. An NURSE input ALZHEIMER STAGE DETAIL to ADD ALZHEIMER STAGE
process and then the process send ALZHEIMER STAGE INFORMATION into
STAGE data store.
2. An NURSE input ALZHEIMER STAGE DETAIL to UPDATE ALZHEIMER
STAGE process and then the process send ALZHEIMER STAGE INFORMATION
into STAGE data store.
3. An NURSE input ALZHEIMER STAGE DETAIL to DELETE ALZHEIMER
STAGE process and then the process send ALZHEIMER STAGE INFORMATION
into STAGE data store.
3.1
Add Alzheimer
Stage
3.2
Update
Alzheimer
Stage
3.3
Delete
Alzheimer
Stage
Nurse
D4 Stage
Alzheimer Stage Details Alzheimer Stage Information
Alzheimer Stage
Details
Alzheimer Stage DetailsAlzheimer Stage Information
Alzheimer Stage
Information
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3.9.5 Manage Questionnaire For Nurse
Figure 3.9: Data Flow Diagram Level 1 for Manage Questionnaire For Nurse
Description :
1. An NURSE input QUESTIONNAIRE DETAIL to ADD QUESTIONNAIRE process
and then the process send QUESTIONNAIRE INFORMATION into
QUESTIONNAIRE data store and invokes STAGE INFORMATION into ADD
QUESTIONNAIRE.
2. An NURSE input QUESTIONNAIRE DETAIL to UPDATE QUESTIONNAIRE
process and then the process send QUESTIONNAIRE INFORMATION into
QUESTIONNAIRE data store and invokes STAGE INFORMATION into UPDATE
QUESTIONNAIRE.
3. An NURSE input QUESTIONNAIRE DETAIL to DELETE QUESTIONNAIRE
process and then the process send QUESTIONNAIRE INFORMATION into
QUESTIONNAIRE data store and invokes STAGE INFORMATION into DELETE
QUESTIONNAIRE.
4.1
Add
Questionnaire
4.2
Update
Questionnaire
4.3
Delete
Questionnaire
Nurse
D4 Stage
D5 Questionnaire
Questionnaire Details
Questionnaire Details
Questionnaire Details
Questionnaire Information
Questionnaire Information
Questionnaire Information
Stage Information
Stage Information
Stage Information
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3.9.6 Manage Result For Patient
Figure 3.10: Data Flow Diagram Level 1 for Manage Result For Patient
Description :
1. An PATIENT input ANSWER QUESTIONNAIRE to ANSWER QUESTIONNAIRE
process and then the process send QUESTIONNAIRE INFORMATION into
QUESTIONNAIRE data store.
2. An QUESTIONNAIRE data store input QUESTIONNAIRE INFORMATION into
GET RESULT process and then ,the process retrieve QUESTIONNAIRE
INFORMATION to RESULT data store.
3. RESULT data store then input RESULT GENERATED into GET RESULT process
which is output RESULT to PATIENT
5.1
Answer
Questionnaire
5.2
Get Result
Patient
D5 Questionnaire
D6 Result
Answer Questionnaire
Result
Generate result
Questionnaire Information
Questionnaire
Information
Questionnaire
Information
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3.9.7 Manage Confirmation For Doctor
Figure 3.11: Data Flow Diagram Level 1 for Manage Confirmation For Doctor
Description :
1. An RESULT data store input CONFIRMATION OF RESULT into GIVE
CONFIRMATION process An DOCTOR input DOCTOR’S CONFIRMATION to
GIVE CONFIRMATION process and then the process send CONFIRMATION OF
RESULT into RESULT data store.
2. A RESULT data store input CONFIRMATION OF RESULT into GET
CONFIRMATION process and then ,the process send CONFIRMATION OF
RESULT to PATIENT
6.1
Give
Confirmation
6.2
Get
Confirmation
Doctor
D6 Result
Patient
Doctor
ConfirmationConfirmation of Result
Result Status
Confirmation of Result
Confirmation of
Result
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3.10 ENTITY RELATIONSHIP DIAGRAM
Figure 3.12 :Entity Relationship Diagram of Early Alzheimer’s Disease Detection System
(one to many) strong relationship
(one to many) weak relationship
An entity-relationship diagram (ERD) show that the entities information and entities
relationship. ERD is consist of identifying and defining the entities, determine entities
interaction and the cardinality of the relationship.
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CHAPTER IV
CHAPTER IMPLEMENTATION AND TESTING
4.1 INTRODUCTION
In this chapter, the implementation of the system will described how the system’s output and
input during the testing phase. Testing are need to avoid any error occur in the future. Early
testing can help in maintaining the system’s performance. In this chapter also, interface of the
system will help the user to understanding the system. Interface is built through the
specifications and requirement in make sure it achieves the objectives.
4.2 IMPLEMENTATION AND OUTPUT
4.2.1 Database Design
Database playing important part in making the data and information in the system display
properly. Database is used to store the data.
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4.2.1.1 Diabetes Prediction System Database.
Figure 4.1: The tables in the database Diabetes Prediction System.
There are seven table available in the database such as doctor, login , nurse, patient,
questionnaire, result and stage. For each table, there are attributes at every column.
4.2.1.2 Table doctor
Figure 4.2 : Table doctor
Table doctor contain DoctorID, Name, PhoneNo, Email and Address. In this table, DoctorID is a
primary key and not null.
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4.2.1.3 Table login
Figure 4.3 : Table login
Table login contain UserID, and Password. In this table, UserID is a primary key and not null.
4.2.1.4 Table nurse
Figure 4.4 : Table nurse
Table nurse contain NurseID, Name, PhoneNo, Email and Address. In this table, NurseID is a
primary key and not null.
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4.2.1.5 Table patient
Figure 4.5: Table patient
Table patient contain PatientID, PatientName, PatientIcNo, PatientYear, PhoneNo,
Address,GuardianName, GuardianPhoneNo ,Relation, GuardianAddress and Email. In this
table, PatientID is a primary key and not null.
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4.2.1.6 Table questionnaire
Figure 4.6 : Table questionnaire
Table patient contain QuesID, Question, StageID, Answer1, Answer2, Mark1 and Mark2. In
this table, QuesID is a primary key, not null and StageID is a foreign key .
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4.2.1.7 Table result
Figure 4.7 : Table result
Table result contain ResultID, PatientID, QuesID, Mark , Confirmation and DoctorID. In this
table, ResultID is a primary key, not null and PatientID, QuesID and DoctorID is a foreign
key .
4.2.1.8 Table stage
Figure 4.8 : Table stage
Table Stage contain StageID, and StageName. In this table, StageID is a primary key, not nul
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4.2.2 Interface Design
4.2.2.1 User Module
Figure 4.9 : Homepage
Figure 4.9 show interface for User homepage. After login, User will enter this page. There
are menu in this page such as Home, About Alzheimers, Manage Profile, Questionnaire, and
Report Status.
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Figure 4.10 : About Alzheimer’s
Figure 4.10 show interface for About Alzheimer. User can see all the information about the
Alzheimer’s disease.
Figure 4.11 : Manage Profile
Figure 4.11 show interface for User Manage Profile. This page consist of two function which
is user can update profile and also they can change password..
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Figure 4.12 : Questionnaire
Figure 4.12 show interface for User Questionnaire. There will be a list of questionnaire about
Alzheimers’s disease detection and user need to answer all the question in order to get the
final result.
Figure 4.13 : Report Status
Figure 4.13 show interface for User Report Status. After answering questionnaire, User can
view their report status here. There will be two option here which is viewing the result or get
the confirmation from doctor. If user want to get the confirmation from the doctor ,they need
to wait for the doctor confirmation in few days and can view in this page too.
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4.2.2.2 Nurse Module
Figure 4.14 : Homepage
Figure 4.14 show interface for Nurse homepage. After login, Nurse will enter this page.
There are menu in this page such as Home, Manage Profile, Manage Questionnaire, and
Report Questionnaire.
Figure 4.15 : Manage profile
Figure 4.15 show interface for Nurse Manage Profile. This page consist of two function
which is Nurse can update profile and also they can change password.
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Figure 4.16 : Manage Questionnaire
Figure 4.16 show interface for Nurse Manage Questionnaire. This page consist of three
function which is Nurse can add Questionnaire, update Questionnaire and also they can delete
Questionnaire.
Figure 4.17 : Report Questionnaire
Figure 4.17 show interface for Nurse Questionnaire Report. Nurse can view the questionnaire
detail in this page.
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4.2.2.3 Doctor Module
Figure 4.18 : Homepage
Figure 4.18 show interface for Doctor homepage. After login, Doctor will enter this page.
There are menu in this page such as Home, Manage Profile, Manage and Patient Report
Status.
Figure 4.19 : Manage Profile
Figure 4.19 show interface for Doctor Manage Profile. This page consist of two function
which is Doctor can update profile and also they can change password.
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Figure 4.20 : Patient Result Status
Figure 4.20 show interface for Doctor Patient Report Status. Doctor can view patient report
status in order to give the confirmation.
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4.3 SOLUTION COMPLEXCITY
4.3.1 Questionnaire Sample
Have you noticed any of these warning signs?
Note: Be aware that it may be difficult to place a person with Alzheimer’s in a specific stage
as stage may overlap.
1. Please state year of patient.
o 64 years below mark = 0
o 65 years abovemark = 1
Mild Alzheimer’s Disease (early - stage)
2. Problem coming up with the right word or name.
o Yes mark = 1
o No mark = 0
3. Trouble remembering names when introduced to new people.
o Yes mark = 1
o No mark = 0
4. Challenges performing task in social or work setting.
o Yes mark = 1
o No mark = 0
5. Forgetting material that one has just read.
o Yes mark = 1
o No mark = 0
6. Losing or misplacing a valuable object.
o Yes mark = 1
o No mark = 0
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7. Increasing trouble with planning or organizing
o Yes mark = 1
o No mark = 0
Moderate Alzheimer’s disease ( middle-stage)
8. Forgetfulness of events or about one’s own personal history
o Yes mark = 1
o No mark = 0
9. Feeling moody or withdrawn, especially in socially or mentally challenging situation.
o Yes mark = 1
o No mark = 0
10. Being unable to recall their own address or telephone number or the high school or
college from which they graduated.
o Yes mark = 1
o No mark = 0
11. Confusion about where they are or what day it is.
o Yes mark = 1
o No mark = 0
12. The need for help choosing proper clothing for the season or the occasion.
o Yes mark = 1
o No mark = 0
13. Trouble controlling bladder and bowels in some individuals.
o Yes mark = 1
o No mark = 0
14. Changes in sleep patterns, such as sleeping during the day and becoming restless at
night.
o Yes mark = 1
o No mark = 0
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15. An increasing risk of wandering and becoming lost.
o Yes mark = 1
o No mark = 0
16. Personality and behavioural changes, including suspiciousness and delusions or
compulsive, repetitive behaviour like hand- wringing or tissues shredding.
o Yes mark = 1
o No mark = 0
Severe Alzheimer’s disease(late-stage)
17. Need round the clock assistance with daily activities and personal care
o Yes mark = 1
o No mark = 0
18. Lose awareness of recent experiences as well as of their surroundings,
o Yes mark = 1
o No mark = 0
19. Experience changes in physical abilities, including the ability to walk, sit and,
eventually swallow.
o Yes mark = 1
o No mark = 0
20. Have increasing difficulty communicating.
o Yes mark = 1
o No mark = 0
21. Become vulnerable to infections , especially pneumonia.
o Yes mark = 1
o No mark = 0
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score stage
0-7 Mild Alzheimer’s Disease (early - stage)
8-16 Moderate Alzheimer’s disease ( middle-stage)
17-21 Severe Alzheimer’s disease (late-stage)
$score = 0;
$score = $ans1 + $ans2 + $ans3 + $ans3 + $ans4 + $ans5 + $ans6 + $ans7 + $ans8 + $ans9 +
$ans10 + $ans11 + $ans12 + $ans13 + $ans14 + $ans15 + $ans16 + $ans17 +$ans18 +$ans19
+$ans20 +$ans21;
If ( ($score =17) ) {
$stage = Severe Alzheimer’s;
}
Elseif (($score =8) ) {
$stage = Moderate Alzheimer’s;
}
Elseif (($score =1) ) {
$stage = Mild Alzheimer’s;
}
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CHAPTER V
CONCLUSION
5.1 INTRODUCTION
This chapter focused on project contribution, constraints of the project and its
conclusion, future works that can be gained from this project. From this project, we can find
out how to improve the system.
5.2 PROJECT CONTRIBUTION
Early Alzheimer’s Disease Detection System was successfully developed before its
timeline. This system very useful to another people. The people can aware about their
potential in getting Alzheimer’s Disease. Not only the patient that should aware, the family
members also need to be aware. This system help user to predict which stage of Alzheimer’s
Disease they suffering. This system also give the confirmation from the doctor to those who
need it.
5.2 PROJECT CONSTRAINTS AND LIMITATION
Every system must be have its own obstacle or difficulties in developing the system.
It can occur on developing phase or design phase. This constraints can effect the schedule for
developing system.
For this system, the difficulties that has been faced is its to hard to find the questions
that suitable for the user in Malaysia in predicting the Alzheimer’s Disease. Although already
found the suitable question, the questions still cannot replace laboratory test from hospital.
This system also cannot generate accurate result as the doctors do.
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5.3 FUTURE WORKS
In the future, there are still a lot work can be made into this system. Firstly, for the
risk prediction, the paginations should be done to make friendly viewed by user. Next, more
information should be added in the system. The system should be have its security to improve
its user privileges.
5.4 CONCLUSION
As conclusion, this system has been implemented by using Decision Tree technique.
With Decision Tree technique, the stage of Alzheimer’s Disease can be identified. The user
can use this system because this system already achieved its objectives. However, the user
still need to go through details examination in hospital accurate result. This system are built
to aware the user about their potential to have Alzheimer’s Disease. An early step can
preserved the disease become worse. Be aware before late.
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5.0 REFERENCES
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Applications (0975 – 8887) , Volume 98– No.10 ―Diagnosis of Breast Cancer using
Decision Tree Data Mining Technique‖ , July
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2. Ms. Leena Patil, Ms. Aparna Sirsat, Ms. Diksha Kamble, Mr.Yogesh Pawar,
International Research Journal of Engineering and Technology (IRJET), Volume: 04
Issue: 02‖ Lung Cancer Detection using Decision Tree Algorithm‖ Feb-2017<
www.irjet.net>.
3. Sandhya Peddabachigari, Ajith Abraham*, Johnson Thomas Department of Computer
Science, Oklahoma State University, USA ―Intrusion Detection Systems Using
Decision Trees and Support Vector Machines‖
4. Alzheimer’s Disease and Dementia |alz.org|Alzheimer’s Association.‖what is
Alzheimer’s.‖ .
5. Alzheimer’s Disease and Dementia |alz.org|Alzheimer’s Association.‖ Stages of
Alzheimer's.‖ .
6. Alzheimer’s Disease and Dementia |alz.org|Alzheimer’s Association.‖ 10 Early Signs
and Symptoms of Alzheimer's.‖ .
7. OR-Notes.‖Decision trees examples‖ J E Beasley
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8. ―Detection of Alzheimer disease in brain images using PSO and Decision Tree
Approach‖. IEEE Xplore Digital Library, 26 January
2015..
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9. ―PHP Decision Tree Classifier: Compose decision trees and evaluate subjects‖.PHP
Classes,.
10. Andrew Powell-Morse ―Iterative Model: What Is It And When Should You Use
Should You Use It?.December 15, 2016 It?‖.
11. Amir Ghahrai ―Iterative Model What is the Iterative Model?‖ July 2nd, 2017<
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12. Wikipedia, ―Decision tree model‖
.