A fuzzy knowledge based system for diagnosis of Malaria

1. .
A FUZZY KNOWLEDGE BASED SYSTEM FOR
CLINICAL DIAGNOSIS OF TROPICAL FEVER
Ismael SEKIZIYIVU
Master’s Thesis Defense
Thesis Committee:
Asst Professor Murat İSKEFİYELİ (Advisor)
Asst Professor .Ali GÜLBAĞ (Co.-Advisor)
Asst Professor . Mehmet Recep BOZKURT(External Faculty)
Department of Computer and Information Engineering
Sakarya University
November 14, 2014

2. Outline
 Introduction
Problem Statement
TROPFEV system
Development Processes
Conclusion

3. Sub-Saharan Africa
 Geographically, the area of the continent of
Africa that lies south of the Sahara Desert.
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

4. 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

5. 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

6. Geographical distribution of
typhoid, 2011
 In 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

7. 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,
1. A knowledge base that represents facts about the
world.
2. The inference engine that represents logical
assertions and conditions about the world.
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion
User Interface
Explanation
Part
Inference
Engine
Knowledge
Acquisition
Knowledge
Base
Comput
er and
User

8. Knowledge based reasoning
techniques
 There are a number of knowledge based reasoning
methods ;
1. semantic network
2. Artificial neural networks
3. case based reasoning
4. rule based reasoning.
5. fuzzy logic which was used in this project
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

9. Fuzzy Logic
 “Fuzzy 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
values
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

10. Fuzzy Logic
 It uses fuzzy set theory that allows all values of a
function in the defined interval [0, 1]
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

11. 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
Vienna
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

12. Problem Statement
 Laboratories ,
modern hospitals and medical
experts are scarce in rural areas
Where 70% of the population
live
 Clinical diagnosis is mostly used due to scarcity
of laboratories.
 The two diseases have various diagnosis features
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

13. Problem Statement
 Some Symptoms of malaria and typhoid are similar
and hence a task in medical diagnosis .
 Using signs and Symptoms
We develop a
system that will help in easier
Decision making and
classification during diagnosis
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

14. The TROPFEV System
The system can
be used by both
Doctors, medical
Workers and has
a easy user
interface
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

15. TROPFEV System Development Processes.
1. Fever domain knowledge source identification
2. Fever knowledge acquisition
3. Fever knowledge representation
4. Designing a fuzzy inference system
5. Implementation of TROPFEV fuzzy inference
system
6. Verification and testing
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

16. 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 it
Satisfactory
?
Roll out
Fever knowledge Acquisition
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

17. 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

18. 2.Fever knowledge Acquisition
Tropical Fever
Typhoid Malaria
Uncomplicated Uncomplicated
malaria
Complicated Complicated
malaria
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion
Malaria and
typhoid fever
appear as
complicated
or
Uncomplicate
d

19. Fever knowledge Acquisition
S/N Code Attribute (Symptom) Category
AGE Age
Group category
PRE Pregnancy
FEV Fever
Symptoms
APP Loss Of Appetite.
CON Convulsions
VOM Vomiting
MEN Altered Mental State
PRO Prostration
ANE Anemia
DEH Dehydration
BRE Difficulty in Breathing
THR Threatening Abortion
CHI Chils
PAI Pain
SPL Splenomegaly
HEA Headache
BRA Relative Bradycardia
ABD Abdorminal Pain
MAL Malaise
COP Constipation
GUP Gut Perforation
21
Diagnosing
features of
Fever where
acquired
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

20. 3.FeverKnowledgeRepresentation
 The diagnosis features were represented using
fuzzy logic reasoning.
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

21. 4.Designing the TROPFEV
Fuzzy inference system
 Defining system input and output variables.
 Linguistic variables and membership functions
 Defining fuzzy rules of the system
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

22. Linguistic variables and membership
functions
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

23. Membership functions
 Graph showing some of the input membership
functions
Input output
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

24. Defining fuzzy rules of the
system
 These 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 form
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

25. Implementation of TROPFEV
fuzzy inference system
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion
The system
was
implemented
in Matlab
2012a

26. Inserting rules in the Rule Editor
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

27. Designinterface in Matlab GUI
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

28. 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

29. Verification and testing
NO AGE PRE FEV APP CON VOM MEN PRO ANE DEH BRE THR CHI PAI SPL HEA BRA ABD MAL COP GUP RESULT
001 6.6 0 4 2 0 0 0 4 4 4 2 0 1 0 0 1 0 0 0 0 0 4.5
002 10 0 4 1 2 0 0 0 2 0 0 0 2 4 2 2 0 2 0 0 0 1.5
003 1.7 0 4.5 1.5 0 2 0 0 2 0 0 0 4 0 0 0 0 0 4 0 0 1.5
004 17 0 1 2 0 1 2.5 1 0 1 0 0 0 1 1 0 0 0 1 2 0 0.5
005 23 0 2 3 0 1 2.5 1 0 2 0 0 0 1 2 2 0 0 1 2 0 0.5
006 20 0 4 0 4 1 4 1 1 1 4 0 2 4 0 0 0 0 4 0 0 4.5
007 16 0 4 4 2 1 1 4 4 3 0 0 2 0 1 2 0 1 0 0 0 4.5
008 42 0 3 1 0 2 2.5 2 0 2 0 0 0 0 2 1 2 2 3 2 0 0.5
009 40 0 2 2 0 1 0 0 0 2 0 0 1 2 2 1 0 0 1 2 0 0.5
010 2 0 4.5 3 1 1 0 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1.5
011 4 0 4 2 0 0 0 0 2 2 0 0 0 0 2 0 0 0 0 0 0 1.5
012 65 0 1 2 0 2.5 2.5 0 0 0 0 0 2 2 2 3 2 1 1 1 0 0.5
013 1.9 0 3.5 2.5 0 0 1 3 2 1 0 0 0 0 0 0 0 0 0 0 0 1.5
014 15 0 4 2.5 0 2 1 2 2 1 0 0 0 0 0 2 0 0 0 0 0 1.5
015 20 0 3.5 3 0 2 0 0 0 3 0 0 2 0 2 1 0 4 0 0 0 2.5
016 18 0 3 3 0 2 2.5 1 0 2 0 0 2 1 2 1 2 2 2 2 0 0.5
017 2 0 3.5 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.5
018 12 0 4 2 0 2 1 1 2 1 0 0 0 0 0 2 0 0 0 0 0 1.5
019 3 0 4.5 1 0 2 0 0 1 0 0 0 4 0 0 0 0 0 4 0 0 1.5
020 12.5 0 4 2 0 2 0 0 2 2.5 0 0 1 3.5 2 1 0 2 0 0 0 1.5
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

30. System performance
Case Doctor’s Diagnosis Expected TROPFEV
001 Complicated malaria 4.5 4.5
002 Uncomplicated malaria 1.5 1.5
003 Uncomplicated malaria 1.5 1.5
004 Uncomplicated typhoid 0.5 0.5
005 Uncomplicated typhoid 0.5 0.5
006 Complicated malaria 4.5 4.5
007 Complicated malaria 4.5 4.5
008 Uncomplicated typhoid 0.5 0.5
009 Uncomplicated typhoid 0.5 0.5
010 Uncomplicated malaria 1.5 1.5
011 Uncomplicated malaria 1.5 1.5
012 Uncomplicated typhoid 0.5 0.5
013 Uncomplicated malaria 1.5 1.5
014 Uncomplicated typhoid 1.5 1.5
015 Uncomplicated malaria 1.5 2.5
016 Uncomplicated typhoid 0.5 0.5
017 Uncomplicated malaria 1.5 1.5
018 Uncomplicated malaria 1.5 1.5
019 Uncomplicated malaria 1.5 1.5
020 Uncomplicated malaria 1.5 1.5
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

31. Testing the TROPFEV system
using user Interface
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

32. Scatter plot for doctor’s
Diagnosis against TROPFEV
showing a positive correlation
Introduction -Problem Statement -TROPFEV system -Development Process- Conclusion

33. Conclusion
 Knowledge 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

34. Future work
 The 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

35. Thank you
 Special thanks to
Examination committee
1. Asst Professor Murat İSKEFİYELİ (Advisor)
2. Asst Professor .Ali GÜLBAĞ (Co.-Advisor)
3. Asst Professor . Mehmet Recep BOZKURT
And the audience at large
 QUESTIONS ???