main project 1 to 3 edited in church

7/23/2019 Main Project 1 to 3 Edited in Church

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CHAPTER ONE

1.1. Background

Diabetes mellitus is the commonest endocrine-metabolic disorder characterized by

chronic hyperglycaemia giving rise to the risk of microvascular (retinopathy,

nephropathy, and neuropathy) and macrovascular (ischaemic heart disease, stroke and

peripheral vascular disease) damage, with associated reduced life expectancy and

diminished quality of life Diabetes mellitus may present with characteristic symptoms

such as thirst, polyuria, blurring of vision, and weight loss !n its most severe forms,

ketoacidosis or a non"ketotic hyperosmolar state may develop and lead to stupor, coma

and, in absence of effective treatment, death (#$%, &''') eople with diabetes are at

increased risk of cardiovascular, peripheral vascular and cerebrovascular disease

everal pathogenetic processes are involved in the development of diabetes *hese

include processes, which destroy the beta cells of the pancreas with consequent insulin

deficiency, and others that result in resistance to insulin action *he abnormalities of

carbohydrate, fat and protein metabolism are due to deficient action of insulin on target

tissues resulting from insensitivity or lack of insulin (#$%, &''') *he prevalence of

diabetes is increasing rapidly worldwide and the #orld $ealth %rganization (+) has

predicted that by + the number of adults with diabetes would have almost doubled

worldwide, from &.. million in + to . million /xperts pro0ect that the incidence

of diabetes is set to soar by 123 by ++45 meaning that the disease will affect a

staggering 4& million citizens (6owley and 7ezold, +&+) *he estimated worldwide

prevalence of diabetes among adults in +& was +84 million (123) and this value is

predicted to rise to around 2' million (..3) by + (haw et al, +&) 6ecent

estimates indicate there were &.& million people in the world with diabetes in the year

+ and this is pro0ected to increase to 11 million by + *his increase in


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prevalence is expected to be more in the 9iddle /astern crescent, ub-aharan :frica

and !ndia !n :frica, the estimated prevalence of diabetes is &3 in rural areas, up to .3

in urban sub-ahara :frica, and between 8-&3 in more developed areas such as outh

:frica and in population of !ndian origin :frica (onny ; et al., 2011). *he

prevalence in


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also influence the risk of developing type + diabetes ;onsumption of sugar-sweetened

drinks in excess is associated with an increased risk (9alik C et al., 2010) *he type

of fatsin the diet is also important, with saturated fatsand trans " fatty increasing the

risk andpolyunsaturated and monounsaturated fatdecreasing the risk /ating lots of

white riceappears to also play a role in increasing risk ($u /: et al., 2012).

*he Diabetes is diseases that has significant burden on and healthcare systems

or this study, researchers had developed

software called Diab-memory to support patients entering their information such as

blood-glucose level, in0ected insulin doses, food intake, well-being and physical

activities *hen, data were remotely synchronized to a central database *he system was

based on ava+ 9obile edition (+9/) and built using state of the art internet

technology

*he study sample was & patients with *&D9 9ean age was 11 years (E&& years)

being in the trail study for three months *he result was focused on patientsF adherence

to the therapy, availability of the monitoring system and the effects on metabolic status


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:s questionnaire shows, the system was accepted in general, and this shows that the

role of information system in the health sector cannot be overlooked

*owards reducing the burden of D9 (ma0orly *+D9) in


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the development of the nations by depriving them of valuable human resources in their

most productive years (#$%, ++) *his is because Diabetes mellitus diseases could

eventually lead to disabilities such as stroke and thus, scarce family and societal

resources are directed to the costly and prolonged medical care of such ones (#$%,

++) *herefore, the challenge of this pro0ect is to understand the risk factors or

variables that are responsible for the likelihood of *ype + Diabetes 9ellitus (*+D9)

disease occurrence and evaluate the likelihood of *+D9 disease based on these

variables

1. Sco!e of the Problem

*his pro0ect is limited in scope by the development of a predictive model for *ype +

Diabetes 9ellitus risk using >uzzy =ogic model

1." A#m and Ob$ect#%e& of the &tud'

*he aim of this study is to develop a model for prediction of *+D9 disease using the

>uzzy =ogic 9odel

*he specific ob0ective of this study is toG

(i) identify variables required for predicting *+D9 disease risk(ii) simulate the model(iii) validate the model

1.( )ethodolog'

!n order to achieve the aforementioned ob0ectives, the methodology approach will be as

followsG

(i) /xtensive review of related work on diabetes mellitus prediction will be

done followed by formal interview with disease expert (/ndocrinologists) to

elicit knowledge on variables relevant for disease risk identification(ii) *he fuzzy logic will be used to develop the predictive model for type +

diabetes mellitus disease risk using the variables identified in (i)(iii) imulate the *ype + Diabetes 9ellitus diseases risk predicting system using

the model in (ii)


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(iv) *he performance of the model will be evaluated using performance metrics

likeG accuracy, sensitivity, precision and recall (&-specificity)

1.* +u&t#f#cat#on of the Stud'

*his study is necessitated by the need to prevent calamitous outbreak of diseases that

may send many to untimely grave with the aim of an early detection system (#$%,

++), is pitched towards prevention, and planned response to this terminal disease *o

gain a better knowledge of disease incidence and risk factors so as to control them@ with

the aim of improving the health care delivery in


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variables and aggregation of the output variables, the software used for the

implementation of the system ;hapter four gives detailed information about the system

design, implementation and the tools used in the development of the system !t also

gives a description of the user interface, which the user uses in interacting with the

system >inally, chapter five concludes the work by stating the summary, conclusion

and recommendation of the work done


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8

CHAPTER T-O

/TERAT0RE RE/E-

2.1 /ntroduct#on

:ccording to Detmer (&''.), epidemiology is the study of the distribution and

patterns of health-events, health-characteristics and their causes or influences in well-

defined populations !t is the cornerstone method of public health research and

practice, and helps inform policy decisions and evidence-based medicine by

identifying risk factors for diseases and targets for preventive medicine and public

policies /pidemiologists are involved in the design of studies, collection and

statistical analysis of data, and interpretation and dissemination of results (including

peer review and occasional systematic review) %ver the past years, epidemiology

has significantly contributed to improve methods used in clinical research and, to a

lesser extent, basic (microbiological, genetic) research (ankowski, &''') 9a0or

areas of epidemiological study include bio monitoring, and comparisons of treatment

effects such as in clinical trials, outbreak investigation, diseases surveillance and

screening (medicine) /pidemiologists rely on a number of other scientific disciplines

such as 7iology (to better understand diseases processes), 7iostatistics (to make

efficient use of the data and draw appropriate conclusions), and /xposure assessment

and ocial science disciplines (to better understand proximate and distal risk factors,

and their measurement (7ourlas et al, &''')

*he advancement in computer technology has encouraged the researchers to develop

software for assisting doctors in making decision without consulting the specialists

directly *he software development exploits the potential of human intelligence such

as reasoning, making decision, learning (by experiencing) and many others :rtificial

intelligence is not a new concept, yet it has been accepted as a new technology in


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computer science !t has been applied in many areas such as education, business,

medical and manufacturing *his pro0ect explores the potential of artificial

intelligence techniques in determining the likelihood of Diabetes mellitus diseases in

an individual given a number of associated risk factors

2.2 #abete& )ell#tu& #&ea&e&

*ype + diabetes mellitus (*+D9) is the commonest form of diabetes affecting more

than '3 of the diabetic population worldwide *here is a rapid upsurge in the

number of diabetic patients and this explosive growth is noted in both urban and rural

areas #ild et al estimated the number of *+D9 patients in the year + at &.2

million and predicted it to increase to 11 million in + Diabetes mellitus (D9) is

a serious condition with potentially devastating complications that affects all age

groups worldwide !n &'84, an estimated million people around the world were

diagnosed with diabetes@ in +, that figure rose to over &4 million@ and, in +&+,

the !nternational Diabetes >ederation (!D>) estimated that .& million people had

diabetes *hat number is pro0ected to rise to 44+ million (or & in & adults) by +,

which equates to three new cases per second (onny ; et al., 2011). *his increase in

prevalence is expected to be more in the 9iddle /astern crescent, ub-aharan :frica

and !ndia !n :frica, the estimated prevalence of diabetes is &3 in rural areas, up to

.3 in urban sub-ahara :frica, and between 8-&3 in more developed areas such as

outh :frica and in population of !ndian origin *he prevalence in


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10

2.2.1 E!#dem#olog' of T'!e 2 #abete& )ell#tu& #&ea&e&

9ortality rates generally appear to be most closely linked to a country?s stage of

epidemiological transition /pidemiological transition, a concept first proposed by

:bdel %mran in the &'.s (%mran, &'.&), refers to the changes in the predominant

forms of diseases and mortality burdening a population that occur as its economy and

health systems develops !n underdeveloped countries at the early stages of

epidemiological transition, infectious diseases predominate, but as the economy,

development status, and health systems of these countries improve, the population

moves to a later stage of epidemiological transition, and chronic non-communicable

diseases become the predominant causes of death and diseases (Haziano et al, +1)

6ecent estimates indicate there were &.& million people in the world with diabetes in

the year + and this is pro0ected to increase to 11 million by + Diabetes is a

condition primarily defined by the level of hyperglycaemia giving rise to risk of

microvascular damage (retinopathy, nephropathy and neuropathy) !t is associated

with reduced life expectancy, significant morbidity due to specific diabetes related

microvascular complications, increased risk of microvascular complications

(ischaemic heart disease, stroke and peripheral vascular disease), and diminished

quality of life *he :merican Diabetes :ssociation (:D:) estimated the national

costs of diabetes in the I: for ++ to be Jus&+ billion, increasing to Jus&'+

billion in ++ (#$%, +1)

2.2.2 Aet#olog' Of #abete& )ell#tu&3 Non4/n&ul#n e!endenc' #abete&

)ell#tu& 5N/)6

diabetes mellitus, which is the predominant form of diabetes and accounts for at least

'3 of all cases of diabetes mellitus (Honzalez et al, +') *he rise in prevalence


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is predicted to be much greater in developing than in developed countries (1'

versus +3) (haw et al, +&)

!n developing countries, people aged 2 to 1 years (that is, working age) are affected

most, compared with those older than 1 years in developed countries (haw et al,

+&) *his increase in type + diabetes is inextricably linked to changes towards a

#estern lifestyle (high diet with reduced physical activity) in developing countries

and the rise in prevalence of overweight and obesity (;han et al, +'@ ;olagiuri,

+&) *here are approximately &2 million people with diagnosed type + diabetes in

the IA (7ennett et al, &''4) *he incidence of diabetes increases with age, with most

cases being diagnosed after the age of 2 years *his equates to a lifetime risk of

developing diabetes of & in & (

6isk factors can be either modifiable or non-modifiable 9odifiable risk factors

include@ smoking, obesity, sedentary lifestyle, and lipid disorders


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12

*he effects of risk factors is multiplicative rather than additive, thus people with a

combination of risk factors (for example, smoking, obesity and hypertension) have the

greatest risk of developing heart diseases !t is important to distinguish between

relative risk (the proportional increase in risk) and absolute risk (the actual chance of

an event) *hus, a 4 year old man with a plasma cholesterol of &mmolKlitre who

smokes 2 cigarettes a day is relatively much more likely to die from coronary

diseases within the next decade than a non-smoking woman of the same age with a

normal cholesterol, but the absolute likelihood of his dying during this time is still

small (high relative risk, low absolute risk) (7lessey, &'84)

roximal risks for *+D9 include those associated with consumption patterns (mainly

linked to diets, tobacco and alcohol use), activity patterns, and health service use as

well as biological risk factors such as increased cholesterol, blood pressure, blood

glucose, and clinical diseases *he >ramingham tudy first centred attention on the

concept of Lrisk factorsM associated with *+D9, and most recently reported

substantial -years risk data showing the accumulation of risk over time (encina et

al, +') !mportantly, risk factors for the incidence of *+D9 and those associated

with *+D9 severity or mortality are not synonymous 6isk factors for incidence

become important starting very early in life and accumulate with behavioural, social,

and economic factors over the life course to culminate in biological risks for *+D9

such as increased blood pressure, blood glucose, and clinical diseases %ver the past

few decades, the effectiveness of early screening and long-term treatment for

biological risks or early diseases has contributed to the sharp declines in D9

mortality seen in many countries ($umink et al, &''.) *he :merican Diabetes

:ssociation Huide to Diabetes 9edical


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mellitus asG age N 24 years, ethnicity, family history, habitual physical inactivity,

overweight (79! N +4 kgKm+), hypertension (N !2K' mm $g in adults), and

previously diagnosed impaired fasting glucose or impaired glucose tolerance, $D=

cholesterol O 4mgKdl) andKor triglyceride level (P+4 mgKdl), polycystic ovary

syndrome, and history of vascular disease

*he recent #$% Hlobal $ealth 6isks 6eport of +' (=opez et al, +1) and the

earlier #orld $ealth 6eport of ++ provide comparable and robust estimates of the

contribution of risks to total mortality and measures of disability (9athers et al, +@

#$%, ++, +'b) 6elatively few ma0or behavioural and biological risk factors

account for *+D9 incidence around the world *obacco use, diet (including alcohol,

total calorie intake, and specific nutrients) and physical inactivity serve as the three

ma0or behavioural risks 7etween them, they account for a significant proportion of

cancer, cardiovascular disease, and chronic respiratory diseases incidence in addition

to D9 ($u et al, +&@ #$%, ++@ Bach et al, +2, +4@ Can Dam et al, +8)

;oncerted action focused on these behavioural risks, along with biological risks such

as high blood pressure, high blood lipids, and high blood glucose, would have a wide

impact on the global incidence and burden of diseases (#$%, +'b) $igh blood

pressure, tobacco use, elevated blood glucose, physical inactivity, and overweight and

obesity are the five leading factors globally !n middle income countries, alcohol

replaces high blood glucose in the top five@ in low income countries, a lack of safe

water, unsafe sex, and under " nutrition are important *hese latter points are related

to both the role of early childhood nutrition in the later onset of cardiovascular disease

and D9 as well as the need to integrate the management of $!CK:!D more closely

with D9 in low-income countries (#$%, +'b)


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2..1 Bod' )a&& /nde83 O%er9e#ght and Obet'

%verweight and obesity are defined as abnormal or excessive fat accumulation that

presents a risk to health : crude population measure of obesity is the body mass

index (79!), a person?s weight (in kilograms) divided by the square of his or her

height (in metres) : person with a 79! of or more is generally considered obese

: person with a 79! equal to or more than +4 is considered overweight (#$%,

+&4) %verweight and obesity are ma0or risk factors for a number of chronic

diseases, including diabetes, cardiovascular diseases and cancer %nce considered a

problem only in high-income countries, overweight and obesity are now dramatically

on the rise in low- and middle-income countries, particularly in urban settings (#$%,

+&4) :ccording to =ebovitz (+2), overweight and obesity is a risk factor for

developing type + diabetes *he best measure of overweight and obesity is the body

mass index (79!) %verweight status, a 79! of equal to or greater than +4 kgKm+,

and obesity, a 79! greater than or equal to kgKm+, have become a problem

throughout the #orld 79! levels of this proportion cause an increased risk of

developing many types of chronic diseases, including type + diabetes mellitus !n fact,

the term QdiabesityQ has been used to demonstrate the close link between type +

diabetes mellitus and obesity

2..2 Smok#ng


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#annamethee, haper, and erry (+&) concluded that smoking is considered a risk

factor for developing type + diabetes mellitus

2.. Age

:ge is an important risk factor in developing cardiovascular and diabetes mellitus

diseases, it is estimated that 8. percent of people who die of coronary heart diseases

are 1 and older (:merican $eart :ssociation, +&) !n the age group of +-22 years,

it was estimated about .3 people had diabetes mostly the type + diabetes mellitus@

while in the age group 24-12 years the number increased to &.3@ and the highest

percentage of +1'3 was found in the age group of N14 years (;entres for Disease

;ontrol and revention, +&&) imilar feature was also observed in /ngland, where

the prevalence of diabetes was increasing with age *he peak prevalence of type +

diabetes can be found in the age group of 14-.2 years with &4.3 in men and &23

in women (helton, +1) *+D9 becomes increasingly common with advancing age

" :s a person gets older@ the body undergoes subtle physiologic changes, even in the

absence of diseases (#$%, +8b)

2.." Ph'cal Act#%#t'

#$% and >:% highlighted the importance of physical activity as a key determinant

of obesity, ;CD, and diabetes (oint #$%K>:% /xpert ;onsultation, +)

hysical activity is defined as any bodily movement produced by skeletal muscles that

require energy expenditure !t has been identified as the fourth leading risk factor for

global mortality causing an estimated + million deaths globally hysical activity is

a key determinant of energy expenditure, and thus is fundamental to energy

balance and weight control, hysical activity reduces risk for cardiovascular

diseases and diabetes and has substantial benefits for many conditions, not only


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those associated with obesity *he beneficial effects of physical activity on the

metabolic syndrome are mediated by mechanisms beyond controlling excess body

weight >or example, physical activity reduces blood pressure, improves the level of

high-density lipoprotein cholesterol, improves control of blood glucose in overweight

people, even without significant weight loss, and reduces the risk for colon cancer and

breast cancer among women (#$%, +2)

2..( 0rban4Rural #fference&

6esidence seems to be a ma0or determinant of type + diabetes in ub-aharan :frica

ince urban residents have &4- to 2 times higher prevalence of type + diabetes than

their rural counterparts *his is attributable to lifestyle changes associated with

urbanization and #esternization Irban lifestyle in :frica is characterized by changes

in dietary habits involving an increase in the consumption of refined sugars and

saturated tut and a reduction in liber intake (9ennen et al +) ohngwi and

colleagues (++) have recently reported an increase in fasting plasma glucose in

those whose lives have been spent in an urban environment, suggesting that both

lifetime exposure to and recent migration to or current residence in an urban

environment are potential risk factors for obesity and type + diabetes mellitus *he

disease might represent the cumulative effects over years of dietary changes, decrease

in physical activity, and psychological stress

*he population of :frica is predominantly rural, but the &''4R+ urban growth

rate was estimated at 2 percent (compared with 4 percent in /urope) *hus, more

than . percent of the population of :frica will he urban residents by ++4 (I:

+) *here will therefore be a tremendous increase in the prevalence of type +

diabetes attributable to rapid urbanization


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2..* :ender

!n the first half of the last century, the prevalence of type + diabetes was higher among

women than among men, but this trend has shifted, so more men than women are now

diagnosed with type + diabetes *his change in the gender distribution of type +

diabetes is mainly caused by a more sedentary lifestyle particularly among men,

resulting in increased obesity $owever, recent data have also shown that men develop

type + diabetes at a lower degree of obesity than women " a finding that adds support

to the view that the pathogenesis of type + diabetes differs between men and women

%bservations of sex differences in body fat distribution, insulin resistance, sex

hormones, and blood glucose levels further support this notion (>Srch, A, +&2) *he

body fat distribution, especially the abdominal visceral fat is associated with increased

type + diabetes risk 7ody fat distribution differs by sex (=ogue et al, +&&), and in

general men have more abdominal fat, whereas women have more peripheral fat "

also denoted as LappleM versus LpearM shape =ooking into the abdominal fat, men

also tend to have more visceral and hepatic fat than women do, whereas women have

more subcutaneous fat than men do !n contrast to visceral fat, subcutaneous fat is

associated with improved insulin sensitivity and is therefore protective against type +

diabetes *hus, the phenomenon that men develop diabetes at a lower body mass

index than women can be explained by the fact that men have more visceral fat for a

given body mass index than women and thereby a higher relative risk for developing

type + diabetes (=ogue et al, +&&)

2.., 7am#l' H#&tor' of #abete&

*here is also ample evidence that type + diabetes has a strong genetic basis *he

concordance of type + diabetes in monozygotic twins is T.3 compared with +"

3 in dizygotic twins (Caleriya = et al, +&) *he lifetime risk of developing the


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disease is T23 in offspring of one parent with type + diabetes, greater if the mother

is affected and approaching .3 if both parents have type + diabetes !n prospective

studies, we have demonstrated that first-degree family history is associated with

twofold increased risk of future type + diabetes (Caleriya = et al, +&) *he

challenge has been to find genetic markers that explain the excess risk associated with

family history of type + diabetes : significant proportion of the offspring of

;ameroonians with type + diabetes have either type + diabetes (2 percent) or !H* (8

percent) (9hanya et al +) : positive family history seems to be an independent

risk factor for type + diabetes, but this was not the case in the ;ape *own study

(=evitt et al, &''), in which family history has not an independent risk factor

2..; Pred#abete&

!n &''. and +, the /xpert ;ommittee on Diagnosis and ;lassification of Diabetes

9ellitus (/xpert ;ommittee on the Diagnosis and ;lassification of Diabetes 9ellitus,

&''., Henuth , et al., 2003) recognized an intermediate group of individuals whose

glucose levels do not meet criteria for diabetes, yet are higher than those considered

normal *hese people were defined as having impaired fasting glucose (!>H) Ufasting

plasma glucose (>H) levels & mgKdl (41 mmolKl) to &+4 mgKdl (1' mmolKl)V, or

impaired glucose tolerance (!H*) U+-h values in the oral glucose tolerance test

(%H**) of &2 mgKdl (.8 mmolKl) to &'' mgKdl (&& mmolKl)V !ndividuals with !>H

andKor !H* have been referred to as having prediabetes, indicating the relatively high

risk for the future development of type + diabetes !>H and !H* should not be viewed

as clinical entities in their own right but rather risk factors for type + diabetes as well

as cardiovascular disease (:D:, +&2)


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2." #agno& of T'!e 2 #abete& )ell#tu& #&ea&e&

!f a diagnosis of diabetes is made, the clinician must feel confident that the diagnosis

is fully established since the consequences for the individual are considerable and

lifelong *he requirements for diagnostic confirmation for a person presenting with

severe symptoms and gross hyperglycaemia differ from those for the asymptomatic

person with blood glucose values found to be 0ust above the diagnostic cut"off value

evere hyperglycaemia detected under conditions of acute infective, traumatic,

circulatory or other stress may be transitory and should not in itself be regarded as

diagnostic of diabetes *he diagnosis of type + diabetes in an asymptomatic sub0ect

should neverbe made based on a single abnormal blood glucose value >or the

asymptomatic person, at least one additional plasmaKblood glucose test result with a

value in the diabetic range is essential, either fasting, from a random (casual) sample,

or from the oral glucose tolerance test (O:TT) !f such samples fail to confirm the

diagnosis of diabetes mellitus, it will usually be advisable to maintain surveillance

with periodic re"testing until the diagnostic situation becomes clear !n these

circumstances, the clinician should take into consideration such additional factors as

ethnicity, family history, age, adiposity, and concomitant disorders, before deciding on

a diagnostic or therapeutic course of action :n alternative to blood glucose

estimation or the %H** has long been sought to simplify the diagnosis of diabetes

Hlycated haemoglobin, reflecting average glycaemia over a period of weeks, was

thought to provide such a test :lthough in certain cases it gives equal or almost equal

sensitivity and specificity to glucose measurement (9c;ance D 6, &''2), it is not

available in many parts of the world and is not well enough standardized for its use to

be recommended at this time


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2.".1 S!ec#f#c Te&t& for T'!e 2 #abete& )ell#tu& S'&tem

*esting enables health care providers to find and treat diabetes before complications

occur and to find and treat prediabetes, which can delay or prevent type + diabetes

from developing :lthough not all tests are recommended for diagnosing all types of

diabetes, but the any one of the following tests can be used for diagnosisG

:&; *est, also called the haemoglobin :&c, $b:&c, or glycol haemoglobin

test

>asting lasma Hlucose (>H) *est

%ral Hlucose *olerance *est (%H**)

6andom lasma Hlucose (6H) *est2.".1.1 A1C Te&t

*he :&; test is used to detect type + diabetes and prediabetes but is not recommended

for diagnosis of type & diabetes or gestational diabetes *he :&; test is a blood test

that reflects the average of a person?s blood glucose levels over the past months and

does not show daily fluctuations *he :&; test is more convenient for patients than

the traditional glucose tests because it does not require fasting and can be performed

at any time of the day *he :&; test result is reported as a percentage *he higher the

percentage, the higher a person?s blood glucose levels have been L: normal :&;

level is below 4.3, and :&; of 4. to 12 3, indicates prediabetes eople

diagnosed with prediabetes may be retested in & year eople with an :&; below 4.

percent may still be at risk for diabetes, depending on the presence of other

characteristics that put them at risk, also known as risk factors eople with an :&;

above 13, should be considered at very high risk of developing diabetes : level of

14 percent or above means a person has diabetesM (


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*he >asting lasma Hlucose (>H) test is used to detect type + diabetes and

prediabetes *he >H test has been the most common test used for diagnosing

diabetes because it is more convenient than the %H** and less expensive (H test measures blood glucose in a person who has fasted for at least 8

hours and is most reliable when given in the morning eople with a fasting glucose

level of & to &+4 mgKdl have impaired fasting glucose (!>H), or prediabetes : level

of &+1 mgKdl or above, confirmed by repeating the test on another day, means a

person has diabetes

2.".1. Oral :luco&e Tolerance Te&t

:ccording to


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*he most common signs and symptoms of diabetes areG

>requent urination

Disproportionate thirst

!ntense hunger

#eight gain

Inusual weight loss

!ncreased fatigue

!rritability

7lurred vision

;uts and bruises donFt heal properly or quickly

9ore skin andKor yeast infections

!tchy skin

Hums are red andKor swollen

>requent gum diseaseKinfection

exual dysfunction (men)


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researchers have employed D* to resolve various biological problems, including

diagnostic error analysis (9urphy, +&), potential biomarker finding (Wu et al, ++@

#on et al, +), and proteomic mass spectra classification (Heurts et al, +4)

7ayesian networks are a probability-based inference model, increasingly used in the

medical domain as a method of knowledge representation for reasoning under

uncertainty for a wide range of applications, including diseases diagnosis (7alla et al,

&'84), genetic counselling ($arris, &''), expert system development (tockwell,

&''), gene network modelling (=iu et al, +1), and emergency medical decision

support system (9D) design (adeghi et al, +1)


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al, +1) *he three-layered 9= with 2 categorical input variables and modified

learning method achieved a diagnosis accuracy of over '3

upport vector machines are a new and promising classification and regression

technique proposed by Capnik and his co-workers (;ortes X Capnik, &''4@ Capnik,

&''4) C9s, developed in statistical learning theory, are recently of increasing

interest to biomedical researchers *hey are not only theoretically well-founded, but

are also superior in practical applications >or medical, clinical decision support and

biological domains, C9s have been successfully applied to a wide variety of

application domains, including 9D for the diagnosis of tuberculosis infection

(Ceropoulos, et al, &'''), tumour classification (chubert, et al, +), myocardial

infarction detection (;onforti X Huido, +4), biomarker discovery (rados et al,

+2), and cancer diagnosis (9a0umder, et al, +4)

*o overcome the limited generalization performance of single models and simple

model combination approaches, more precise model combination methods, called

YYen&emble method&


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Diabetes is known as one of the most common diseases that has significant

burden on patients and healthcare systems


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input data (ie independent variables) that have a measurable causal or coincident

relationship to the output !n order for predictive modelling to be useful in a given

application, two fundamental principles must holdG

i%utcomes must have some level of predictability from known data *hat is, similar

patterns represented across model inputs should be indicative of similar outputs@

ii *here exist some measurable relationship between the set of known data values

that will be used as model inputs and the resulting output value(s) that the

model is tasked to approximate@ and

iii 6elationships that existed in the past will continue to hold in the future such that

it is reasonable to use past observations to infer future behaviour

#hen these principles are adhered to, predictive modelling can approximate the

relationship between the known input data measures and the resulting output

2.(. Pred#ct#%e modell#ng a!!l#cat#on&

*here are generally two classes of predictive modelling applications that differ by the

type of output the model producesG

#. 7oreca&t#ng3>orecasting model generate outputs that are continuous-valued *hat is,

the output should be a value ranging from the minimum to the maximum

allowed *hese models are used in applications such as forecastingKestimatingG

sales, volumes, costs, yields, rates, temperatures, scores, etc and

##. Cla&f#cat#on3 ;lassification models generate outputs that are &-of-n discrete

possible outcomes %ften there is a single output that represents a 7oolean (ie,

yesKno) outcome *hese models are used in pattern recognition applications to

do fraud detection, target recognition, vote forecasting, prospect classification,

churn prediction, bankruptcy prediction, etc *his is the preferred methodology

for the implementation of the predictive model for the intended system


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2.* Related -ork&

: few number of prediction systems exists concerning Diabetes 9ellitus and other

related diseases such as ;ardiovascular diseases prediction with varying factors and

data mining methodology applied

2.*.1 A )ach#ne earn#ng A!!roach to Pred#ct#ng Blood :luco&e e%el& for

#abete& )anagement

*his system, Aevin et al (+&2), describe a solution that uses a generic

physiological model of blood glucose dynamics to generate informative features for a

upport Cector 6egression model that is trained on patient specific data *he new

model outperforms diabetes experts at predicting blood glucose levels and could be

used to anticipate almost a quarter of hypoglycaemic events minutes in advance

:lthough the corresponding precision is currently 0ust 2+3, most false alarms are in

near-hypoglycaemic regions and therefore patients responding to these

hypoglycaemia alerts would not be harmed by intervention (Aevin et al., +&2)

2.*.2 /ntell#gent Heart #&ea&e& Pred#ct#on S'&tem 5/HPS6 ung -e#ghted

A&&oc#at#%e Cla&f#er&

yoti et al (+&&) designed the !$D system as a HI! based !nterface to enter the

patient record and predict whether the patient is having $eart diseases or not using

#eighted :ssociation rule based ;lassifier *he prediction is performed from mining

the patient?s historical data or data repository !n #eighted :ssociative ;lassifier

(#:;), different weights are assigned to different attributes according to their

predicting capability *he system has been implemented in 0ava latform and trained

using benchmark data from I;! machine learning repository *he system is

expandable for the new dataset


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*he system is a #eb-based, user-friendly, scalable and reliable that can be

implemented in remote areas like rural regions or countryside, to imitate like human

diagnostic expertise for treatment of heart ailment *he system is expandable in the

sense that more number of records or attributed can be incorporated and new

significant rules can be generated using underlying Data 9ining technique resently

the system has been using & attributes and records only and the data is from I;!

machine learning dataset that is mainly used for research purpose :s the symptoms

that cause a particular disease may vary from region to region, the system should be

trained using local dataset collected from the clinic

2.*. ec#on Su!!ort #n Heart #&ea&e& Pred#ct#on S'&tem 5SHPS6 ung

Na=%e Ba'e&

*he D$D was developed by ubbalakshmi et al (+&&) using or, example it can

incorporate other medical attributes besides the one used !t can also incorporate other

data mining techniques ;ontinuous data can be used instead of 0ust categorical data


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Table 2.1 Table of related 9ork& to #abete& )ell#tu& Pred#ct#on

S>N Author5&6 Re&earch T#tle Sco!e Strength& #m#tat#on& Remark&

& Aevin ,

6azvan 7,

;indy 9, ay

, and >rank

: (+&2)

: 9achine

=earning

:pproach to

redicting 7lood

Hlucose =evels

for Diabetes

9anagement

7lood

Hlucose

=evels

*he system incorporate

upport Cector

6egression (C6) model,

informed by a

physiological model and

trained on patient specific

data, has outperformed

diabetes experts at

predicting blood glucose

levels and can predict

+3 of hypoglycaemic

events minutes in

advance

*he C6 system was

able to predict +3 of

the hypoglycaemic

events with a false

positive rate under &3

*he system performs

prediction using blood

glucose datasets collected

from *ype & D9

patient?s and C6

model with hysiological

features

+ yoti oni,

Izma :nsari,

Dipesh

harma,

unita oni(+&&)

!ntelligent and

/ffective $eart

Disease

rediction

ystem using#eighted

:ssociative

;lassifiers

$eart

Disease

redictio

n

*he system incorporates

patient health record with

a detailed genetic

analysis *here is a need

to combine these factorsto provide a better overall

determinant of risk

*he prediction is

performed from mining

the patient?s historical

data, which is from

I;! machine learningdataset, which is

mainly used for

research purpose

*he system performs

prediction using patient?s

health history and

-e#ghted :ssociation

rule based ;lassifier

Hubbalaksh

mi, 9*ech,

A 6amesh,

9*ech, 9

;hinna 6ao,

hD (+&&)

Decision

upport in $eart

Disease

rediction

ystem using


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disease !t is implemented

as web based

questionnaire application

and can serve as a training

tool to train nurses and

medical students to

diagnose patients

with respect to ease of

model interpretation,

access to detailed

information and

accuracy

2 runa,


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CHAPTER THREE

RESEARCH )ETHOOO:?

.1 /ntroduct#on

*he research methodology focused on the identification of the different variables required

for predicting the risk of *+D9 in patients from pecialist in the ;ollege of 9edicine,

%bafemi :wolowo Iniversity, !le " !fe via the use of structured interview followed by the

formulation of the fuzzy logic " based model for predicting the risk of *+D9 in such

patients through the use of 9:*=:7 fuzzy logic toolbox

.2 ar#able& e&cr#!t#on

!n this study, the work is limited to six paramount risk factors of the *+D9 only since the

work is intended to provide a system, which aids preventive medicine via the earlier

detection of the disease risk *he causatives variables of *+D9 were classified according to

the groups that they belong to and may only be used to identify the status of the individual

risk to these groups (see *able &)

*he risk factors of those set of variables that help in the identification of the risk of *+D9

includeG

i 7ody 9ass !ndex (79!)G this is a measure of the ratio of the height (in meters) to

the square of the weight (in Ag) used in identifying the likelihood of obesity *he

risk of diabetes and cardiovascular disease increases and the body mass index

increasesii :geG this is another ma0or determinant of the *ype + Diabetes 9ellitus disease

because the higher the age (from years old) the higher the likelihood of the

*+D9 diseaseiii >amily $istory of DiabetesG *his is another identification of the existence of

family members who have had *+D9 or are still living with the disease *he risk


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of *+D9 increases with the existence of family members especially the first

generation membersiv 7lood ressureG this is the measure of systolic and diastolic blood pressure of the

individual and has a benchmark *he risk of *+D9 increases with the increase in

blood pressurev $istory of Hestational DiabetesG Hestational diabetes is the type of diabetes that

usually affects the women during pregnancy *he risk of *+D9 increases with

patient that has had occurrences of Hestational Diabetesvi HenderG *he recent data have also shown that men develop type + diabetes at a

lower degree of obesity than women " a finding that adds support to the view that

the pathogenesis of type + diabetes differs between men and women

%bservations of sex differences in body fat distribution, insulin resistance, sex

hormones, and blood glucose levels further support this notion (>Srch, A, +&2)


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Table .1 R#&k 7actor& A&&oc#ated 9#th T2)

S>N R#&k 7actor& abel&

& >amily $istory of *+D9


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. The mathemat#cal model u&ed for T2) !red#ct#on

*he front-end via which the user will be communicating with the system requires certain

rules which consists of a combination of values of labels of each risk factors required by the

system in determining the status of the patients? *ype + diabetes mellitus status *he >uzzy

=ogic model used in developing the *+D9 prediction system is a qualitative computational

approach, which describes uncertainty or partial truth >uzzy logic has ranging value of

and & that corresponds to the Ldegree of truthM /very set that does not reflect a crisp set, but

has clearly defined boundary is a fuzzy set >uzzy sets represents simple linguistic concepts

like yes-no, true-false, low-medium-high, etc : given element may belong to more than one

fuzzy set at the same time, because the theory of fuzzy sets us a theory of graded concepts

and membership elasticity (!dowu :, et al, 2015) :ll fuzzy sets are characterized by

membership functions " Ya curve that defines how each point in the input space is mapped to

a membership value or degree of membership between and & *he input space is

sometimes referred to as the universe of discourse? (9athwork, +&&)

>or the purpose of this study, there is need to make a general description of the mathematical

model of the proposed fuzzy logic model that was used *he mathematical model of the

fuzzy logic was used to generate the membership functions that were used to map the label

of each variable to their respective fuzzified value using a process called fuzzification *he

membership function that was used in this study in fuzzifying the variables (input and

output) is the triangular membership function in equation &@this function maps the label of

each variable using a triangular " shaped function which uses three () points to define the

two (+) base points and one (&) apex point *he apex point is usually defined by using a

parameter between the two base points

*he mathematical representation of the triangular membership function used to map the

labels of each variables (input and output) is as followsG


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f(x ; a , b , c)=

{

0,x axaba

,a x b

cxcb

, b x c

0,c x

(3.1)

or

f(x ; a , b , c )=max(min(xaba,cxcb ) ,0) (3.2)

#here a and c are the base of the triangle andb is the apex point of the triangle, andx is the

label value within the interval a and c

!n this study, all variables were divided into three labels each represented by its own

triangular membership functions defining their respective base points and apex points *hus,

in the process of fuzzification all variables were mapped using the following membership

functions as defined below, for the three () labels of each variable and that of two (+) labels

of each variable as follow in equations , 2, 4, 1, and . respectivelyG

>or the variables with two labels, the membership functions will beG

label1= f(x ;0.00, 0.25,0.5 ) .(3.3)

label2=f(x ;0.50,0.75,1.00 ) ..(3.4)

#hile for the variables with three labels, the membership functions will beG

label1= f(x ;0.00, 0.16,0.33 ) ..(3.5)

label2=f(x ;0.33,0.49,0.66 ) .(3.6)

label3= f(x ;0.66, 0.83,1.00 ) .(3.7)


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." >u@@' og#c )odel for Pred#ct#ng T'!e 2 #abete& )ell#tu& #&ea&e R#&k

*he fuzzy logic model for predicting the risk of *+D9 involves the process of fuzzification

" defining the input and output variables in the >uzzy !nference ystem (>!), construction

of rule-based for the inference engine, the aggregation of the rules and then the

defuzzification of the results of the aggregated membership function

*he first process in modelling a fuzzy logic system is >uzzification, and this is used to

convert each of input data to a degree of membership function in the 9:*=:7 fuzzy logic

toolbox *hus, the triangular membership function is chosen for fuzzification of both inputs

and output variables !n the process of fuzzification, each input data was mapped with the set

of rules to establish the degree of fitness on how each rule matches the particular input !t is

to be noted that the triangular membership function was used to map the degree of

membership of the labels of each variables used for input and output variables

*he schematic representation of the fuzzy logic system for *+D9 disease risk predicting

system in figure & below shows the set of variables used as inputs ofr the model and the

risk as the output variable for the system


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T2DM DISEASE RISK INFERENCE ENGINE

Using

l#$% &'D (o)y-ass-In)$* ! +al#$% &'D (&g$ ! +al#$% &'D (History-of-,$stational-Dia$t$s ! +al#$% &'D (loo)-.r$ss#r$ ! "al#$% &'D (,$n)$r ! +al#$% TH/' (

#l$ 1

#l$ 2

#l$ 3

#l$ '

#l$ '

&TI' (Us$ Triangular Membership Function to ma t$ +arial$s to t$ir r$s$ti+$ la$l

Famil !istor o" Diabetes

#o$ Mass In$e% MI'

Age

!istor o" Gestational Diabetes

#loo$ (ressure

Gen$er

AGGREGATI)N

&&ll #l$s ar$ aggr$gat$) into a singl$ f#i:$) o#t#t +arial$Ty$ 2 Dia$t$s $llit#s is .r$)i

(;o


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38

.( S#mulat#on En%#ronment

*he schematic representation of the *ype + Diabetes 9ellitus Disease 6isk inference engine

shown in the figure & above shows different factors of *+D9 as input to the fuzzy logic

model for determining the risk of the disease (*ype + Diabetes 9ellitus), and the output

variable is determined by the fuziffication of the input variables using *riangular

9embership function to map the variables to their respective label *able & shows the

description of the fuzzification of input variables for *+D9 Disease using the mathematical

model in equation & to plot the fuzzified values and equations and 2 for variables

with two labels, while equations 4, 1, and . for variables with three labels *able +

shows the fuzzification of the input variables (ie risk factors) needed for determining the

risk of *ype + Diabetes 9ellitus disease

*he simulation environment for the *ype + Diabetes 9ellitus Disease risk predicting system

was carried out using the 9:*=:7 :! *he formulation of the model was done by using

the 9:*=:7 fuzzy logic toolbox *he 9:*=:7 fuzzy logic toolbox contains fuzzy

inference system (>!) editor that was used to define both the input and output variables

*he input variables consist of six (1) input labels with three () or two (+) triangular

membership function as shown in figure + below, while the output variables consist of

membership functions *he rule editor interface was used for the rule-based of the interface

inference engine of >! showing the relationship between the six (1) input variables and the

output variables using !> " *$/< rules *he :


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Table .2 The 7u@@#f#cat#on of T2) #&ea&e R#&k /n!ut 5R#&k 7actor6

S>N R#&k 7actor& abel& 7u@@' og#c alue

& >amily $istory of *+D9


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40

>igure + >uzzy !nference ystem for rediction of risk of *+D9


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41

.* S'&tem Reu#rement

ystem requirements are an important aspect of system development and they are used to

specify details of system functions, services and the basis for designing the system *hese

requirements were used to discover and clarify the function of the system *his segment

consists of the feasibility study, the specification and analysis of requirements, and pro0ect

definition *herefore, the scope of the system requirement of the predicting system covers

the following areasG

i !dentify the factors affecting diabetes mellitus disease and their corresponding

influence *his is to highlight the factors that are considered to be associated with

diabetes mellitus disease and how significance their influence is so that an accurate

predictive model can be formulatedii 6epresent and document the activities to be carried out by the type + diabetes

mellitus disease risk predicting system and the corresponding entities :fter

identification of the factors and their corresponding influences, there is a need to

represent the activities and entities involved with the system and document the

informationiii Henerate a model using fuzzy logic approaches *he model was developed by

identifying the variables that are required in type + diabetes mellitus diseaseiv Develop a prototypical type + diabetes mellitus disease risk predicting system with

using fuzzy set approaches /fforts were made to ensure that the system is able to

predict the likelihood of occurrence of diabetes mellitus disease

*he system aim to assist doctor in predicting the patient type + diabetes mellitus disease risk

status thereby reduces the number of people coming to the hospital and easing the doctor?s

task !t will also allow people to know how prone they are to developing type + diabetes

mellitus disease without visiting the hospital based on their body mass index, blood

pressure, sedentary lifestyle, health history and their current health status, though some

information will still be needed from the doctor for accurate prediction


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42

., S#mulat#on Tool&

>or the simulaton of the proposed model, the >uzzy =ogic *oolbox available in the

9:*=:7 6+&a software will be used *he 9:*=:7 >uzzy =ogic *oolbox consist of

>! editor, 9embership >unction /ditor, 6ule /ditor, 6ule Ciewer, urface Ciewer, and the

>uzzy !nference ystem (>!) at the centre of the whole system (Mathworks, 2013).

i >uzzy !nference ystem /ditor3 *he 9:*=:7 fuzzy logic toolbox contains fuzzy

inference system (>!) editor that was used to define both the input and output

variables *he >! /ditor handles the high-level issues for the system by determining

the number of input and output variables alongside their names *he >uzzy =ogic

*oolbox does not limit the number of inputs $owever, the number of inputs may be

limited by the available memory of the machine !f the number of inputs is too large,

or the number of membership functions is too big, then it may also be difficult to

analyse the >! using the other HI! tools##. *he 9embership >unction /ditor is used to define the shapes of all the membership

functions associated with each variable###. *he 6ule /ditor is for editing the list of rules that defines the behaviour of the

system#%. *he 6ule Ciewer and the urface Ciewer are used for looking at, as opposed to

editing, the >! *hey are strictly read-only tools *he 6ule Ciewer is a 9:*=:7

based display of the fuzzy inference diagram shown at the end of the last section

Ised as a diagnostic, it can show (for example) which rules are active, or how

individual membership function shapes are influencing the resultsv *he urface Ciewer is used to display the dependency of one of the outputs on any

one or two of the inputs R that is, it generates and plots an output surface map for

the system


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43

7#gure . 70DD? O:/C TOOBO


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44

.; S'&tem O!erat#onal Reu#rement&

.;.1 Hard9are Reu#rement&

>or the proper functioning of the diabetes mellitus disease risk prediction system, the

following items will be needed for the hardwareG

a : ;omputer with internet access and at least a entium !!! processor@

b :n input and pointing device@

c : hard Disk of at least &H7 of size is required in order for the repository to run well

without congesting other programs@ and

d 6andom :ccess 9emory of at least 4&+97 is required

.;.2 Soft9are Reu#rement&

*he following software will be needed for the proper functioning of the diabetes mellitus

disease risk prediction systemG

a) #indows %perating ystem (#ins . and above)

a) 9:*=:7 >uzzy =ogic *oolbox


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45

CHAPTER 7O0R

T?PE 2 /ABETES )E/T0S /SEASE R/SF )OE EEOP)ENT

".1 S#mulat#on of the 7u@@' og#c )odel for Pred#ct#ng T'!e 2 #abete& )ell#tu&

#&ea&e R#&k

*he simulation of the fuzzy logic model for the prediction of *ype + Diabetes 9ellitus

disease risk was simulated using the fuzzy logic toolbox available in the 9:*=:7 +&

Development /nvironment Ising the formulated triangular membership functions defined

for each input and output variable, the membership functions and the respective fuzzy

inference model for the risk of *+D9 using six risk factors as shown in figure + above

were used as the inputs *he triangular membership functions in figures


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46

>igure 2G 9embership function of >amily $istory


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47

>igure 2&G *riangular 9embership >unction of :ge


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>igure 2+G *riangular 9embership >unction for 7ody 9ass !ndex


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>igure 2G *riangular 9embership >unction for $istory of Hestational Diabetes


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>igure 22G *riangular 9embership >unction for 7lood ressure


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>igure 24G *riangular 9embership >unction for Hender


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>igure 21G *riangular 9embership >unction for the risk of *+D9


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RE7ERENCES

:D: +&2G :merican Diabetes :ssociationG Diagnosis and ;lassification of Diabetes

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:kinkugbe %% *he non-communicable diseases in


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>Srch, Aristine Hender and *+D9 UinternetV +&2 :ug &@ Diapedia &2'.+8&1 rev no

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Ceropoulos, A, ;ristianini,


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#$% (+8b)G Y*he global burden of disease@ +2 update? Heneva

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