Proceedings of the 6th WSEAS International Confere~puter Science. Hangzhou. China. April 15-17. 2007 83

VHCS: A WEB-BASED E_HEALTIISYSTEM FOR ALOPECIADIAGNOSIS

SIn SORA YA ABDUL RAHM}~l\~ ~Z:nAR OTHMAN, TEO WAI SIEFaculty ofcomputer.Scle?ty Op.~fonnation Technology

UntverSl lVlalaya50603 Kuala LUlllpur

MALAYSIAfazidah@UIll.edu III twsisiti_soraya@um.edu.my, . y, sle@yahoo.com

Ab . d developmentof. stract: - This work presents the deSign~ or "hair loss" . a web-based e-health system that aims to

dlag~~se different types of common alopeciair losSdisease !l.sease. The paper describes the process ofacqu~tng and codifying the knowledge of?~ tree graph ti tng expert system approach. The acquireddomatn. knowledge is modeled using declsl~nto knowled ollowed by representation in the form ofproduction rules before it is transformed In ngine Us ~e base. The system architecture and its

. f ence e 'er lnte fcomponents such as knowledge base, 10 er . ges for difti r ace, and explanation functions are alsod~scribed. The system has 152 rules and 49 l::Oon non-sc er~nt types of symptoms and diseases. It candiagnose up to 17 types of diseases of most co arrlng alopecia.

X stem, Web-basedEx7Y-Words: - E-Health System, Expert ys pert System, Knowledge Base, Alopecia

Diagnosis

1 IntrodUctionE-Health is defined as the application of Internet:md other related technologies in the heal~Caretndus~ to improve the access, efficI~n~~~effectiveness and quality of clinical and bus~n sproc~s~es utilized by healthcare organi~UO~practitioners, patients, and consumers Ineffort to improve health status of patients [4].

N dWowadays, e-health system not only use~tore, manipulate and retrieve data; the~ :u-etncreasingly taking on the role of assistinghuman decision making. Technologies used f~e-health. system have been widely explo~)Inte~atmg Artificial Intelligence. ( thetechniques is one example in supportmg AIsystem. One of the major contributions !hat AIhas made is in the area of problem solvmg· .programs that exhibit expert-level competence InI . arrowso Vtng problem in a domain of n

specialization are called expert systems. ~expert . peclficsystem contains domam-sknowledge derived from the human exp~rthDomain refers to the problem area for whl~knowledge is captured in an expert system. T emain 1 rt systemgoa of the development of expe

istopro·d .availableVI e expertise of domain expert widelyAs for ~d transferred to a computer program.sYste this system, named VHCS', the expertand :; app~oach helps in providing solutionshal ggestions for the treatment to the relatedair loss di

H. Iseases being diagnosedall 1 .

gend oss problem occurs world-wide in bothers of all .eXperi ages. Generally, people Will

day. ~~e of hair shedding of about 50 to 100 alIowe s amount of hair loss is normal.am ver, excessive of hair loss beyond this

ount indi t haialope·. ca es air problem. Hair loss orhair fjClaISa disorder which involves the loss ofrom skiface i n areas such as from the head scalp,of h :e. eyelashes, eyebrows, beards, and lossappe:r on body. Hair is important to thePeopleance. of male and female. Therefore,Careful tlth ~opecia suffer tremendously.will aid i~~nosl~ of the type of alopecia disease

electIng the right treatment solution.

I

VIicS stand fi V·s or Irtual Hair Care Specialist.

84 Proceedings of the 6th WSEAS International Conference on Applied Computer Science. Hangzhou. China. April 15-17. 2QQ1

2 Domain BackgroundThe domain knowledge is essential for thedevelopment of an expert system. There areseveral different types of hair loss, each with adifferent cause. Hair loss can be categorized into-two types: (1) Non-cicatricial or non-scarringalopecia, (2) Cicatricial or scarring alopecia [8].Non-scarring alopecia cause temporary hair loss,hair follicles remain undamaged thus potentiallyreversible. Causes of non-scarring alopeciainclude iron deficiency, severe stress,pregnancy, or pulling hair. Scarring alopecia isassociated with destruction of hair follicles thusirreversible hair loss. Causes of scarring alopeciainclude scalp diseases, bacterial infections ofhair follicles, and trauma such as burns,r-adiation, and chemical injuries.

The diagnosis scope of the system is limitedto 17 types of diseases of common non-

scarring alopecia:

Alopecia Universal isii Alopecia Totalisiii Alopecia Areataiv Alopecia Barbaev Monilethrixvi Trichophagiavii Trichotillomaniaviii Ophiasisix Sisaiphox Traction Alopeciaxi Loose Anagen Syndromexii Telogen Effluviumxiii Anagen Effluviumxiv Female Pattern Baldnessxv Male Pattern Baldness (Stage 1)xvi Male Pattern Baldness (Stage 2)xvii Male Pattern Baldness (Stage 3)

3 Knowledge AcquisitionKnowledge acquisition (KA) refers to theprocess of acquiring, studying, and organizingthe domain knowledge [2]. The KA objective is

to compile a body of knowledge on the problemof interest to be encoded in an expert system.Acquiring knowledge from the domain experthas always been regarded as the "bottleneck" inthe development of an expert system because itremains the most difficult task [2] as adaptedfrom Hayeth-Roth et al., 1983. This is mainlydue to communication problems between theknowledge engineer (KE) and the expert, theinability of the expe. to verbalize theknowledge and the difficulty of the KE toextract knowledge from the expert [2].In this project, the KA process consists of

expert interview. The author who acted as K£interviews a consultant trichologist. The expertprovides valuable information to assist theauthor in developing the system. Furthermore,references from specialized books [1] [5] [6] andfrom different websites [7] [8] [9] appropriate tothe domain were also considered. ContinuoUSprocess of knowledge acquisition through rapidprototyping was used so as to ensure sufficientand concrete knowledge is acquired. ThiSstrategy not only aids KE to represent thedomain knowledge correctly, but also assists theexperts to validate the system's reasoningprocess and its results.

The acquired domain knowledge is thenanalysed, modeled using a decision tree graph,before finally being represented in the form ofpro~u~tion rules in the knowledge base.Decision tree graph is a diagram consisting ofn?des and branches that depicts the solution to 11

gtV~n.problem. Figure 1 and Figure 3 depict thedecision tree graph for backward and forwardchaining reasoning respectively. Backward-chaining is described as top-down reasoningbecause it reasons from hypotheses down to thelow level facts (evidence) which support thehypotheses. In contrast, forward-chaining isreferred to as bottom-up reasoning since itreasons from low level problem facts to the toplevel conclusions that are based on the facts [3).

Proceedings of the 6th WSEAS International Conference on Applied Computer Science. Hangzhou. China. April 15-17. 2007 85

Intennedlateh~TKHypotheses

Fig. 1Backward chaining decision tree

4 SystemDevelopment

Design and

4.1 System ArchitectureThe system is a web-based application.Public can access to the system at anytimeand any place, wherever an Internetconnection is available. The system is basedupon client/server three-tier architecture.The three layers are client tier, applicationtier and data tier. The client web browsersupports the user interface. The serversoftware in an application tier supports theinference mechanism of the system. Thisserver side inference capability isprogrammed using PHP scripting languageand developed over Microsoft Windows XPProfessional platform.

The server-side knowledge base in datatier plays a vital role in the systemdevelopment. It is used for storing rules andfacts needed for disease diagnosis. Theknowledge base is designed with MySQLServer Database. Figure 2 shows the three-tier web application architecture.

DataTI •• ©MySQLD .... _

;Application ~

Tier ";:::

"'"w.tt S.rver with PHF

:CII.mT~~~

~Usersof.tt. Wlb ~ff:2we Surfer.1 w.b Surt.r.3

FIg. 2 Three-tier system architecture

4.2 System componentThe following sub-sections explain thedifferent component ofVHCS in brief.

4.2.1 Knowledge BaseThe knowledge base contains set ofsymptom-consequent pairs in the form of IF-THEN rules, also known as productionrules. The decision tree graphs depicted inFigure 1 and Figure 3 above is firstconverted into production rules before it isstored in a knowledge base. The system hasover 152 rules for different types of diseasesand symptoms.

86 Proceedings of the 6th WSEAS International Conference on Applied Computer Science. Hangzhou. China. April 15-17.2007

'0.'ft•s..£..

II)1:II)...-c0';;;'uII)"0coe'2';..c:o"Eco~...0~~00~

Proceedings of the 6th WSEAS International Conference on Applied Computer Science. Hangzhou. China April 15-17. 2007 87

Id AND_Part1 AND_ParaIF_Parta number of hairs drop

per day greater than100

THEN_Partproblem IS hair lossproblem

b number of hairs drop baldness-patches ma~made IS no problem IS hair lossper day le$$ than appear IS yes problem100

c number of hairs drop baldness-patches hair thinning- problem IS hair lossper day IeBS than appear IS no shedding IS yes problem100

d number of hairs drop hairs drop greaterper day than 100

'e number of hairs drop hairs drop l88sper day than 100

f baldness-patches baldness-patchesappear appear =yes

g baldness-patches baldness-patchesapp .. r appear "no

h hair thinning- hair thinning-shedding shedding =yes

number of hairs dropper day greater than100

number of hairs dropper day less than 100

baldness-patchesappear IS yes

baldness-patchesappear IS no

hair thinning-sheddingISY88

Fig. 4 Sample of system production rules

4.2.2 Inference EngineThe inference engine of the system emulateshuman expert reasoning process. It derivessome conclusions from the problem factscontained in the working memory and thedomain knowledge contained in theknowledge base. Working memory is thecomponent that contains the problem factsof a given session, both entered by the userand inferred by the system along withconclusions drawn by the system.The system employs both backward

chaining and forward chaining inferencetechnique to perform diagnosis. The systemwill first try to prove or disapprove ahypothesis - is the user facing hair loss? Thesystem would then test the hypothesis byobtaining problem data and attempts toconclude a hypothesis from this data - typeof hair loss disease. The system usebackward chaining for the first task andforward chaining for the second task. Figure1 and 3 illustrates chaining graph structuresfor backward and forward chaining,respectively.

4.2.3 U er InterfaceThe interface serves as medium throughWhich the user communicate with the

system. The interface requests information,obtain required answers from the user andoutputs intermediate and final results [2].

The interaction with the system is throughsome form of natural language with amultimedia user interface. User can selectsimple textual display and/or image thatillustrate the symptoms. The multimediainterface is employed so as to guide user tocome to a decision by choosing matchingsymptom images with symptomdescriptions. This, in turn increases theaccuracy of disease diagnosis.

4.2.4 Explanation SubsystemThe system incorporates explanationfacility, the ability of a system to explain itsreasoning. The function of this facility is toreveal WHY a question is asked during aconsultation and HOW the system reached aconclusion. WHY query is made by showingthe rule which it is trying to prove. Thesystem needs to know the premises of acertain rule are true so that the conclusioncan be inferred. HOW query accessible atthe end of every consultation is justified bytracking back through the rules thatestablished the conclusion.

88 Proceedings of the 6th WSEAS International Conference on Applied Computer Science. Hangzhou. China. April 15-17. 2.Q!l1

5 System Testing andImplementationSystem tests, such as system validation anduser acceptance were carried out as acontinual process throughout the project.System validation was carried with the helpof domain expert in an attempt to validatethe system's knowledge and its reasoning.F or the user acceptance test, the system wasmade available to the user and theircomment was gathered by conducting userfeedback survey. The primary objectives ofuser acceptance test were to test theinterface and to determine the impact of howwell the system addresses the needs of theuser [2]. An analysis of systemimplementation results reveals that:• In general, the system has been an

effective aid to user to diagnose hair lossdisease. The system reasons in a waysimilar to that of expert and draws thesame diagnosis results as the humanexpert.

• Identified diseases, final results andsuggested prescription on treatment arevery informative and useful.

• The multimedia user interface is veryeffective. The user finds it easier andmore accurate to tell symptoms bymatching symptom images withsymptoms description.

• Explanation subsystem is welcomed andregarded as very convenient tool foruser.

6 ConclusionThis paper presents the design anddevelopment of a web-based e-health systemfor alopecia diagnosis. The presentedexample shows that the diagnosingprocedure generated by the system ispractical and the system does work.However, the system in its present versionhas a number of weaknesses which will beaddressed in future work:• The system is limited to diagnose up to

17 types of common non-scarringalopecia. As a result, the intent to extendand augment the knowledge base for

more types of diseases covering scarringalopecia is anticipated.

• Diagnosing merely on signs andsymptoms. For more accurate result,hair analysis is necessary. The hairanalysis results are needed to confirmand support the hair loss diseasediagnostic process.

The current version of the system was aproof-of-concept prototype. Through severalprototype trials, VHCS proves that an expertsystem could be designed successfully forhair loss domain. .

References:[1]Baden, H. (1987). Diseases of the Hairand Nails. United State of America: YearBook Medical Publishers, Inc.

[2]Durkin, J. (1994). Expert systems: Designand development. New York: MacmillanPublishing Company.

[3] Giarratano, J., Riley, G. (1998). Expertsystems: Principles and programming (3rded.).United State of America: PWSPublishing Company.

[4]Jennifer Marconi, E-Health: Navigatingthe Internet for Health InformationHealthcare, Advocacy White Paper.Healthcare Information and ManagementSystems Society, May, 2002.

[5]Olsen, E. (1994). Disorders of HairGrowth: Diagnosis and Treatment. UnitedState of America:McGraw-HilI, Inc.

[6]Thomson, J. & Thomson, L. (1967).Healthy Hair: Care and restoration bynatural methods. United State of America:Thorsons Publishers LTD.

[7]http://www.wrongdiagnosis.comla/alopecialbook-disease -12a.htm

[8]http://\Wlw.skinstreet.netlalopecia.htm!