applying fuzzy logic and neural network to rheumatism treatment in oriental medicine

8/3/2019 Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine

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Thang, C. et al.

Paper:

Applying Fuzzy Logic and Neural Network to RheumatismTreatment in Oriental Medicine

Cao Thang , Eric W. Cooper , Yukinobu Hoshino , and Katsuari Kamei

Graduate School of Science and Engineering, Ritsumeikan University

1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

E-mail: [email protected] College of Information Science and Engineering, Ritsumeikan University

1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

E-mail: [email protected], [email protected] Dept. of Electronic and Photonic System Engineering, Kochi University of Technology

185 Miyanoguchi, Tosayamada, Kami, Kochi 782-8502, Japan

E-mail: [email protected]

[Received October 31, 2005; accepted March 31, 2006]

In this paper, we present an application of soft

computing into a decision support system RETS:Rheumatic Evaluation and Treatment System in Ori-

ental Medicine (OM). Inputs of the system are severi-

ties of observed symptoms on patients and outputs are

a diagnosis of rheumatic states, its explanations and

herbal prescriptions. First, an outline of the proposed

decision support system is described after considering

rheumatic diagnoses and prescriptions by OM doc-

tors. Next, diagnosis by fuzzy inference and prescrip-

tion by neural networks are described. By fuzzy infer-

ence, RETS diagnoses the most appropriate rheumatic

state in which the patient appears to be infected, then

it gives a prescription written in suitable herbs withreasonable amounts based on neural networks. Train-

ing data for the neural networks is collected from ex-

perienced OM physicians and OM text books. Finally,

we describe evaluations and restrictions of RETS.

Keywords: neural network, fuzzy inference, oriental

medicine

1. Introduction

Rheumatism is an arthritis disease widespread in all

Vietnamese population groups, unfortunately influencing

socioeconomic aspects of Vietnam. Among all soft tis-

sue diseases, rheumatism accounts for 15% and the most

common rheumatic type, joint degeneration, accounts for

10% [1]. In Vietnam, therapeutic treatments for rheuma-

tism are physical methods, anti-inflammatories and orien-

tal medicine (OM). Among these, OM is an indispens-

able part because it has fewer side-effects than west-

ern medicine and gives good treatment results. Besides,

herbal prescriptions are easy to find and relatively cheap

in comparison with western drugs. The number of Viet-

namese patients treated by OM is about 50%.

Accurate diagnoses have an important role in treating

diseases. Building a successful decision support systemsuch as RETS based on knowledge from experienced OM

physicians will help moderate evaluations in rheumatic di-

agnoses, which tend to be subjective. It will indirectly

help physicians to provide the right treatment to the right

patients, improving the quality of the health care services

as a whole. It also will help qualified and experienced

physicians in OM to maintain and share their profound

knowledge with colleagues and to assist medical students

or young physicians, especially those living and working

in rural areas.

In the last 50 years, the advent of the computer has

greatly stimulated developments of Artificial Intelligence(AI), especially Expert System (ES) and Decision Sup-

port System (DSS) which perform the roles of a specialist

or assist people in carrying out works requiring specific

expertise. Since the beginning of AI, ES and DSS have

been successfully applied to Western Medicine (WM),

and then to OM with reasoning techniques including Un-

certainty Reasoning [5, 6], Fuzzy Logic [24, 7, 8], Case-

based Reasoning [9, 10] and Neural Network [13, 14].

Most of the ES and DSS in OM focused on problems of

disease diagnoses and analyses with the specific charac-

teristics of OM such as four inspection steps, the six inter-

nal organs and the viscera, Yin and Yang [2, 7, 8]. A few

recent researches are concerned with integrating WM and

OM [11], and modifying treatment herbal prescription in

OM [12] using Fuzzy Logic.

This research presents an application of Fuzzy Logic

and Neural Network (NN) into RETS: Rheumatic Evalu-

ation and Treatment System in OM. In diagnosing stage

of RETS, based on severities of observed symptoms on

patients and importance degrees of clinical symptoms in

each rheumatic state, fuzzy inference is used to decide

which rheumatic states the patient has. Then in the pre-

scribing stage, the fuzzy severities will be put into a cor-

responding NN to get an appropriate herbal prescription.

The importance degrees of clinical symptoms are evalu-

4 Journal of Advanced Computational Intelligence Vol.11 No.1, 2007

and Intelligent Informatics


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Applying Fuzzy Logic and NN to Rheumatism Treatment in OM

Fig. 1. Diagram of diagnosing and prescribing rheumatism

by OM doctors.

ated by rheumatism treatment experts. Training data for

the NN is typical treatment prescriptions collected from

experienced doctors and OM text books.

2. Outline of RETS

According to OM, each disease has specified states and

each disease-state is cured by standard herbal prescrip-

tions. Such a prescription is easily found in medical text

books or OM reference books. With a patient, standard

prescriptions become much more efficient and effective if

physicians add more suitably additional herbs to it and

then adjust all of its herbal amounts. The herbal ad-

justments are often based on the severities of observedsymptoms on the patient, and their effects mainly depend

on physicians treatment experience. Only experienced

physicians can give patients suitable prescriptions with

reasonable adjustments.

In OM, rheumatism primarily consists of 12 disease

states and 32 typical clinical symptoms. The number of

rheumatic treatment herbs is 63 [15]. Based on the severi-

ties of observed symptoms, doctors diagnose and classify

rheumatic states, then give a corresponding herbal pre-

scription with reasonable amounts in grams. Fig. 1 shows

the process of diagnosing and prescribing rheumatism by

OM doctors. Such a process can be suitably assisted by a

DSS or an ES as shown in Fig. 2 [17].Roles of the functional parts in Fig. 2 are as follows:

Knowledge Acquisition: Surveys symptoms, prescrib-

ing rules, explanations and sample prescriptions.

Knowledge Base: Consists of symptoms, disease

states, inference rules, training data and explanations.

Fuzzy Inference: Checks rules, calculates weights and

advises the most serious rheumatic state.

Neural Networks: Gives prescriptions with reasonable

herbal adjustments.

Interface: Obtains symptoms and their severities from

users and shows inferential results.

Explanation: Helps users to understand OM, rheuma-tism, and explains the results.

Fig. 2. DSS for diagnosing and treating rheumatism in OM.

3. Diagnosing by Fuzzy Inference

In OM, physicians usually give herbal prescriptions

based on the severities of clinical symptoms such as high

fever, slightly numb joints, moderately yellow urine etc.

These ambiguous expressions of symptoms make it un-

suitable for traditional quantitative approaches to build a

DSS for OM. Fuzzy sets, known for their abilities to deal

with vague variables using membership functions rather

than with crisp values, have proven to be one of the most

powerful approaches to resolve this problem. They also

enable developers to use linguistic variables and build

friendly user interfaces. OM physicians usually explaindiagnosing procedures with such expressions as this pa-

tient has these typical symptoms with these severities, so

I prescribe these herbs with these amounts. These ex-

pressions can be represented quite naturally in IF-THEN

fuzzy rules. In addition, fuzzy rules can give expert-like

explanations, making it easier for doctors to understand

the DSS.

So far, based on fuzzy logic many practical applications

in both WM and OM have been built [2, 7, 8], including

rheumatic disease [3, 4].

In RETS, from severities of observed symptoms on the

patient and importance values of the symptoms, fuzzy in-

ference is used to determine which rheumatic states the

patient has

3.1. Symptom and Rule Expression

Suppose that rheumatism has m clinical symptoms, l

rheumatic states. A rheumatic state is determined by n

clinical symptoms.

Let SO

SO1 SOm be a set of observed symptoms

on a patient where SOi is a fuzzy proposition representing

a symptom.

Let H

H1 Hl be a set of the rheumatic states.

Let SR

j S

Rj1

S

Rjn

be a set of symptoms inpremise of rule Rj j 1 l where Rj is generally

Vol.11 No.1, 2007 Journal of Advanced Computational Intelligence 5



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Thang, C. et al.

Fig. 3. Diagram of inference procedure.

described in the following form:

IF SRj1 and S

Rj2 and and S

Rjn

THEN the rheumatic state is Hj . . . . . (1)

Let the two following fuzzy values in SOi

and SRj

ibe

defined:

SOi

0

1

: truth value ofSOi given by doctors when

diagnosing. SOi

1 means SOi clearly appears on the

patient, SOi

0 means SOi does not appear on the

patient, and 0 SOi 1 means SOi appears on the

patient with severity SOi

.

S

Rji

0

1

: importance value of SRji for rheumatic

state Hj given by skilled doctors via survey in ad-vance, where

n

i 1

S

Rji

1 . . . . . . . . . . . . (2)

S

Rji

0 means SRji totally does not affect Hj, S

Rji

1

means SRji is the only symptom affecting Hj, and 0

S

Rj

i

1 means SRji affects Hj with fuzzy importance

S

Rji

.

3.2. Fuzzy Inference Process

If an observed symptom SOi is found in the premise of

rule Rj, premise weight wjSi

ofSOi in Rj is calculated as:

wjSi

SOi

S

Rji

. . . . . . . . . . . . (3)

where

is a t-norm operator, x

y

x

y

in RETS.

If symptoms SRj

ofRj match with observed symptomsSO, weight wRj ofRj is calculated as:

wRj Si S

Rj SO

wjSi

. . . . . . . . . . (4)

where is a t-conorm operator, this t-conorm should be

compatible with (2), x y x y in RETS.

Then RETS finds the most serious state H having the

largest wRj value among l rheumatic states

H

hm wRm maxj

wRj . . . . . . . (5)

Figure 3 shows the diagram of the inference procedure

in RETS. When the inputs, severities of observed symp-toms, are matched with one or more rheumatic states, the

system finds the most appropriate rheumatic state H cor-

responding with these inputs. If the inputs are not enough

to match with any rheumatic state, RETS gives advice

about the closest rheumatic state. In this case, the patient

may have diseases other than rheumatism.

4. Prescribing by Neural Networks

NN is an effective technique to help doctors to analyze,

model and make sense of complex clinical data across abroad range of medical applications [13]. It enables in-

telligent systems to learn from experience, examples and

clinical records, improving performance of the systems

over time. Based on knowledge accumulated from expe-

rienced doctors and hospital information systems, NN can

wisely give doctors good decisions, helping to moderate

subjective evaluation in diagnosing and prescribing dis-

eases.

So far many useful NN applications have been devel-

oped [13, 14]. In RETS, trained by rheumatic treatment

knowledge collected from skilled OM doctors, NN is used

to give herbal prescriptions with reasonable amounts.

An important point in preprocessing training data is toselect the right sets of input and output features. Raw

data are prescription rules and herbal treatment prescrip-

tions with typical observed severities gathered from ex-

perienced doctors. Features should be reasonably chosen

so that from trained NNs we can get appropriate prescrip-

tions in accordance with observed symptoms and diag-

nosed rheumatic states.

For the inputs, there are two types of symptoms. The

first type is associated with Boolean values: Yes (true,

coded by 1) and No (false, coded by 0). Observed severi-

ties in the second type are associated with 5 linguistic val-

ues in company with fuzzy intervals: no (0.00), slightly(0.25), moderately (0.50), relatively (0.75) and clearly




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(1.00).

For the outputs, there are two main kinds of herbs in

a prescription, treating and conducting herbs. Treating

herbs directly cure the infected disease while conducting

herbs help patients organisms to easily absorb herbal ef-

fects. The total number of herbs in a prescription is from

9 to 15. Amounts of treating herbs are often adjusted by

severities of the observed symptoms whereas amounts of

conducting herbs are normally unchanged as in the stan-

dard prescriptions. The number of observed symptoms

used to adjust herbs in a standard prescription is often

from 6 to 12. In training data for NNs of RETS only

symptoms that affect herbal adjustments are used for in-

puts and all of treating and conducting herbs are used for

outputs.

For the same rheumatic state, prescriptions by differ-

ent doctors might not look similar because some doctors

use some herbs but others prefer equivalent herbs that also

give the same effects but come in different amounts. To

avoid using many equivalent herbs for the same prescrip-

tions in training data, lists of herbs in the standard pre-scriptions from text books of Hanoi Medicine University

are used and clarified by experienced doctors. For user

reference, equivalent herbs are written in the description

part of sample prescriptions.

Depending on each rheumatic state, the amounts of

herbs in prescriptions vary from 2 to 60 grams. The error

in the adjusted amounts of an herb accepted by doctors is

usually 0.5 gram for small amounts and 1.0 gram for large

amounts. Since the output range of NN is chosen from 0

to 1, amounts of herbs are normalized as coefficients. The

coefficient ck of amounts of herb k in training data, and

the actual amount WPk

of herb k in the prescriptive results

is calculated as:

ck WT

k W . . . . . . . . . . . . . (6)

WPk ck W . . . . . . . . . . . . . (7)

where WTk is amount of herb k in training data and W is

the maximum amount of a herb in the prescription.

5. Explanations

One of indispensable features of ES and DSS is ca-

pabilities to offer explanations. Logical explanations of

RETS can help users, especially young doctors or medical

students, to deeply understand inference results. Explana-

tions also make it easier for experienced doctors to revise

related sample cases in training data. Currently RETS has

general and detailed explanations about rheumatic states

and prescriptions.

General explanation: after fuzzy inference process,

from the knowledge base RETS obtains a general

explanation about the most serious rheumatic state,

then shows a fuzzy graph of all related states and

fuzzy weights wRj of rules.

Detailed explanation about similar cases: in train-ing data RETS finds similar cases that have same

Fig. 4. One neural network in RETS.

severities of observed symptoms and same infected

rheumatic states with the diagnosed patient, then

shows prescriptions of these cases and their expla-nations from experienced doctors.

6. Implementation

Based on the text books, a preliminary survey and

real rheumatic prescriptions from experienced doctors in

Thaibinh OM College, we have assessed important fuzzy

values of symptoms in rheumatic states, chosen standard

prescriptions from the text books, clarified additional and

equivalent herbs, selected specific symptoms that affected

herbal adjustments, then generated 5,000 doctor-like pre-

scriptions with combinations of severities of the state-

specified symptoms using doctor-prescribing rules and

linear methods with ranges of herbal adjustments. Train-

ing data for NN are the generated prescriptions together

with 460 real rheumatic prescriptions from the experi-

enced doctors.

Since most of herbs are written in Chinese and Viet-

namese, some herbs have English or French names and

some do not, in RETS the names of herbs are written

in Vietnamese. Symptoms and general explanations are

written in both Vietnamese and English. Detailed expla-nations with OM terms are written in Vietnamese.

There are 12 networks corresponding with 12

rheumatic states. Each NN has 3 layers as shown in

Fig. 4. Inputs to NNs are state-specified symptoms

SOi i 1 2 m with SOiand outputs from NNs are

coefficients ck k 1 2 p of amounts of herbs. The

number of neurons in the hidden layer is equal to the num-

ber of output neurons

l

p

. NNs are back-propagation

networks adopting sigmoid or hyperbolic tangent acti-

vated functions. To accelerated training, adaptive learning

and momentum term are also used. Figs. 5 and 6 show the

interfaces of RETS for diagnosis and prescription results,respectively.




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Thang, C. et al.

Fig. 5. Interface of diagnosis.

Fig. 6. Interface of prescription results and explanations.




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7. Evaluations

Combining NN and fuzzy inferences, we can have a

more powerful and effective DSS with learning, reason-

ing and explaining capabilities for evaluating and treating

rheumatism.

The inference rule given by (1) is equivalent to the fol-

lowing rule form:

IF fuzzy severities of symptoms

SRj1 is S

Rj1

and

and SRjn is

SRjn

THEN rheumatic state is Hjwith certainty factor wRj

. . . (8)

Using the rule form (8), a DSS may need thousands

inference rules with many combinations of symptoms in

premises. Not only do they take much time for develop-

ers to accomplish the rule acquisition but also much effort

for domain experts to revise all of the rules. Using the

rule form (1) with (2), RETS uses just 12 inference rules.

Experienced doctors have confirmed that the inference re-sults are quite good and it is easy to review the knowledge

presented by the rules.

In our experiment, we randomly split training data into

two parts of 80% and 20% and used the former for train-

ing and the later for testing. All of nonlinear relations

(real prescriptions and rules of prescribed herbs) as well

as linear relations (ranges of herbal adjustments) were

well learnt by NNs. Depending on the number of inputs

and outputs, each NN can learn about 1000 prescriptions

within an accuracy of 10 2 mean square error with both

training and testing data (equivalent to error of 0.1 gram

for each herb).

In case of unknown inputs, RETS shows the fuzzygraph of infected rheumatic states, recommends the most

proper state in which the patient seems to be infected and

gives explanations by fuzzy inference, then shows the ad-

vised prescription with appropriate amounts of herbs by

NN. Most of these prescriptions are completely compati-

ble with the real prescriptions, prescribing rules and linear

ranges of herbal adjustments in the training data.

In an evaluation with doctors in Hanoi Oriental

Medicine Institute, we asked the doctors for consider-

ing and then giving 50 rheumatic cases including real

cases that they have treated, then compare RETS re-

sults with the doctors opinions. All prescriptions fromRETS could be practically used said by doctors. About

the herbal adjustments including additional herbs and

amounts of herbs in the final prescriptions, 94% prescrip-

tions from RETS are totally agreed and 6% are fairly ac-

cepted. Experienced doctors have also used RETS to il-

lustrate treatments of clinical rheumatism cases for medi-

cal students.

8. Conclusions

We built RETS: Rheumatic Evaluation and Treatment

System in OM, and showed the diagnosing system by

fuzzy inference and herbal prescribing system by NN.

We confirmed that RETS has high performance and high

applicability for diagnosing and prescribing rheumatism.

Unfortunately, like other DSS and ES which are restricted

to a narrow domain of expertise, RETS is developed for

diagnosing and prescribing rheumatism only. It lacks

much real knowledge of human philosophy [16]. If a pa-

tient has other diseases besides rheumatism, doctors can-

not solely rely on this system since they do not have ev-

idence to control potential effects of the herbal prescrip-

tions on the other concurrent diseases. Hence, it is rec-

ommended that the system be used only for patients with

rheumatism alone, not for those with other concurrent dis-

eases.

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Thang, C. et al.

Name:Cao Thang

Affiliation:Graduate School of Science and Engineering,

Ritsumeikan University

Address:1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

Brief Biographical History:1994 Bachelor of Electronics, Hanoi University of Technology

2005 MS, Graduate School of Science and Engineering, Ritsumeikan

University

Main Works:

A Decision Support System for Rheumatic Evaluation and Treatment in

Oriental Medicine Using Fuzzy Logic and Neural Network, Lecture

Notes in Artificial Intelligence (LNAI), Vol.3558, pp. 399-409,

Springer-Verlag, Berlin Heidelberg, 2005.

Membership in Learned Societies: Vietnamese Fuzzy Society

Name:Eric W. Cooper

Affiliation:Associate Professor, Computer Science,

College of Information Science and Engineering,


Address:1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

Brief Biographical History:1998 MS, Ritsumeikan University

2002 Ph.D., Ritsumeikan University

2003 Joined COE program as post-doctoral researcher

2006- Associate Professor, Ritsumeikan University

Main Works: Modeling Designers Color Decision Processes Through EmotiveChoice Mapping, Lecture Notes in Computer Science, Springer-Verlag,

Vol.3558, pp. 410-421, 2005.

Membership in Learned Societies:

Human Interface Society

Institute of Systems, Control and Information Engineers

Association for Computing Machinery

Name:Yukinobu Hoshino

Affiliation:Associate Professor, Department of Electronic

and Photonic system Engineering, Kochi Univer-

sity of Technology

Address:185 Miyanoguchi, Tosayamada, Kami, Kochi 782-8502, Japan

Brief Biographical History:2002 Ph.D., Ritsumeikan University

2002- Assistant Professor in Ritsumeikan University

2006- Associate Professor in Kochi University of Technology

Main Works:

An Application of FEERL (Fuzzy Environment Evaluation

Reinforcement Learning) to Lights OutGame and Avoidance of Detour

Actions in Search, Transactions of the Institute of Systems, Control and

Information Engineers, Vol.14, No.8, pp. 395-401, 2001.

A Proposal of Reinforcement Learning with Fuzzy Environment

Evaluation Rules and Its Application to Chess, Journal of Japan Society

for Fuzzy Theory and Systems, Vol.13, No.6, pp. 626-632, 2001.

Membership in Learned Societies: Japan Society for Fuzzy Theory and Systems

The Institute of Systems, Control and Information Engineers

The Society of Instrument and Control Engineers

Japan Society of Kansei Engineering

Name:Katsuari Kamei

Affiliation:Professor, Human and Computer Intelligence,

College of Information Science and Engineering,


Address:1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan

Brief Biographical History:1983- Assistant Professor, Ritsumeikan University

1993- Associate Professor, Ritsumeikan University

1998- Professor, Ritsumeikan University

Main Works:

Townscape Colour Planning System Using an Evolutionary Algorithm

and Kansei Evaluations, Proceedings of 2006 IEEE InternationalConference on Fuzzy Systems, pp. 4322-4329, 2006.

A Fuzzy Clustering Based Selection Method to Maintain Diversity in

Genetic Algorithms, Proceedings of 2006 IEEE Congress on

Evolutionary Computation, pp. 10364-10369, 2006.

Membership in Learned Societies:

Japan Society for Fuzzy Theory and Intelligent Informatics

The Institute of Systems, Control and Information Engineers

Human Interface Society Japan

Japan Society of Kansei Engineering

IEEE Computational Intelligence Society



applying fuzzy logic and neural network to rheumatism treatment in oriental medicine

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