Masters Thesis Defense

.A Fuzzy Knowledge Based System for Clinical Diagnosis of Tropical Fever

Ismael SEKIZIYIVUMasters Thesis Defense

Thesis Committee: Asst Professor Murat SKEFYEL (Advisor)Asst Professor .Ali GLBA (Co.-Advisor)Asst Professor . Mehmet Recep BOZKURT(External Faculty)

Department of Computer and Information EngineeringSakarya UniversityNovember 14, 2014

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Outline IntroductionProblem StatementTROPFEV systemDevelopment ProcessesConclusion

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Sub-Saharan AfricaGeographically, the area of the continent of Africa that lies south of the Sahara Desert.

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

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Tropical fever Malaria and typhoid fever are the major tropical fever infection

Malaria is caused by mosquitoes

Typhoid fever is caused by Salmonella typhi.

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

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Countries and areas at risk of malaria transmission, 2011 In the tropics , malaria approximately causes 3,000 deaths each day

Introduction - Problem Statement -TROPFEV system - Development Process - Conclusion

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Geographical distribution of typhoid, 2011In sub-Saharan Africa it is estimated to cause 725 cases and 7 deaths per 100,000 person-year

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Knowledge Based Systems

A KBS uses knowledge embedded in a knowledge base to solve complex problems.

A knowledge-based system has at least one and usually two types of sub-systems, A knowledge base that represents facts about the world.The inference engine that represents logical assertions and conditions about the world.Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

User InterfaceExplanation PartInference EngineKnowledge Acquisition

Knowledge BaseComputer and User

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Knowledge based reasoning techniquesThere are a number of knowledge based reasoning methods ;semantic networkArtificial neural networkscase based reasoning rule based reasoning.fuzzy logic which was used in this project

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

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Fuzzy LogicFuzzy logic refers to a logic of approximation. Boolean logic assumes that every fact is either entirely true or false. Fuzzy logic allows for varying degrees of truth. It can be used to represent vague and imprecise ideas, such as mild, high or severe.It uses natural words in the place of numerical valuesIntroduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Fuzzy LogicIt uses fuzzy set theory that allows all values of a function in the defined interval [0, 1]

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Related work The first medical system to use fuzzy logic was CADIAG-2 . It was developed by Prof. K.-P. Adlassnig and his colleagues at the University of Vienna Medical School from the early 80's .Today it is a central subject of research at the Institute for Medical Expert and Knowledge-Based Systems at the Medical University of ViennaIntroduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Problem StatementLaboratories , modern hospitals and medical experts are scarce in rural areasWhere 70% of the population liveClinical diagnosis is mostly used due to scarcity of laboratories.The two diseases have various diagnosis features

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Problem StatementSome Symptoms of malaria and typhoid are similar and hence a task in medical diagnosis .

Using signs and SymptomsWe develop asystem that will help in easier Decision making and classification during diagnosis

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

The TROPFEV System

The system can be used by bothDoctors, medicalWorkers and has a easy user interface

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

TROPFEV System Development Processes.

Fever domain knowledge source identificationFever knowledge acquisitionFever knowledge representationDesigning a fuzzy inference systemImplementation of TROPFEV fuzzy inference system Verification and testing

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

TROPFEV System Development Process.

Knowledge source

Fever knowledge representation Fuzzy inference system design

FIS implementation & Interface design

TROPFEV Verification and testing

Fever knowledge Acquisition

Is itSatisfactory ?

Roll outFever knowledge Acquisition

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Fever domain

1.knowledge source identification

Document using the Uganda clinical guideline 2012

Consulting expert in tropical medicine here Dr. Akusa Yuma Darlington was consulted

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

2.Fever knowledge Acquisition

Tropical FeverTyphoidMalariaUncomplicatedUncomplicated malariaComplicatedComplicatedmalariaIntroduction -Problem Statement -TROPFEV system -Development Process- ConclusionMalaria and typhoid fever appear as complicated or Uncomplicated

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Fever knowledge AcquisitionS/NCodeAttribute (Symptom)CategoryAGEAgeGroup categoryPREPregnancy FEVFeverSymptomsAPPLoss Of Appetite.CONConvulsions VOMVomitingMENAltered Mental State PROProstration ANEAnemia DEHDehydrationBREDifficulty in BreathingTHRThreatening Abortion CHIChilsPAIPainSPLSplenomegalyHEAHeadacheBRARelative Bradycardia ABDAbdorminal PainMALMalaiseCOPConstipationGUPGut Perforation

21 Diagnosing features of Fever where acquired Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

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3.Fever Knowledge Representation

The diagnosis features were represented using fuzzy logic reasoning.

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4.Designing the TROPFEV Fuzzy inference system

Defining system input and output variables.Linguistic variables and membership functionsDefining fuzzy rules of the system

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Linguistic variables and membership functions

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Membership functionsGraph showing some of the input membership functions Input output

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Defining fuzzy rules of the systemThese rules in the rule base were created considering all the possible circumstances and the conditions that were mentioned in the Uganda clinical guidelines 2012 for both malaria and typhoid fever in their complicated and uncomplicated formIntroduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Implementation of TROPFEV fuzzy inference system

Introduction -Problem Statement -TROPFEV system -Development Process- ConclusionThe system was implemented in Matlab 2012a

Inserting rules in the Rule Editor

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Design interface in Matlab GUI

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Verification and testing

Fever cases for 20 patients collected from Arua regional referral hospital in northern Uganda with the help of a medical expert has been used to test to the system. And compare the diagnosis of the real expert with that of the TROFEV system.Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Verification and testing

NOAGEPREFEVAPPCONVOMMENPROANEDEHBRETHRCHIPAISPLHEABRAABDMALCOPGUPRESULT0016.6042000444201001000004.500210041200020002422020001.50031.704.51.5020020004000004001.500417012012.5101000110001200.500523023012.5102000122001200.500620040414111402400004004.500716044211443002012010004.500842031022.5202000021223200.500940022010002001221001200.5010204.53110211000000000001.50114042000022000020000001.50126501202.52.5000002223211100.50131.903.52.5001321000000000001.501415042.5021221000002000001.50152003.53020003002021040002.501618033022.5102002121222200.5017203.52010000000000000001.501812042021121000002000001.5019304.51020010004000004001.502012.5042020022.50013.521020001.5

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System performanceCaseDoctors DiagnosisExpectedTROPFEV001Complicated malaria4.54.5002Uncomplicated malaria1.51.5003Uncomplicated malaria1.51.5004Uncomplicated typhoid0.50.5005Uncomplicated typhoid0.50.5006Complicated malaria4.54.5007Complicated malaria4.54.5008Uncomplicated typhoid0.50.5009Uncomplicated typhoid0.50.5010Uncomplicated malaria1.51.5011Uncomplicated malaria1.51.5012Uncomplicated typhoid0.50.5013Uncomplicated malaria1.51.5014Uncomplicated typhoid1.51.5015Uncomplicated malaria1.52.5016Uncomplicated typhoid0.50.5017Uncomplicated malaria1.51.5018Uncomplicated malaria1.51.5019Uncomplicated malaria1.51.5020Uncomplicated malaria1.51.5

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Testing the TROPFEV system using user Interface

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Scatter plot for doctors Diagnosis against TROPFEV showing a positive correlation

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

ConclusionKnowledge for knowledge based systems is limited from only experts but can also be obtained from documents.Fuzzy logic is good in systems where data is not available.Rules are easy to be edited or added whenever new knowledge is attained though it is tiresome when they come so many.The use of fuzzy logic in medical diagnosis can be more emphasized for its accuracy. The need for tropical fever decision support systems in tropical medicine is vital.Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Future workThe system can also be integrated by adding laboratory tests for malaria and typhoid fever as well as the therapy part.

Constructing such systems as web based medical expert systems can save many lives of people including tourists and remote based patients.

Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

Thank you Special thanks to Examination committeeAsst Professor Murat SKEFYEL (Advisor)Asst Professor .Ali GLBA (Co.-Advisor)Asst Professor . Mehmet Recep BOZKURT And the audience at large QUESTIONS ???