An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems.
The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP.
Uzoka
Faith-Michael Emeka
FM
Department of Computer Science and Information Systems, Mount Royal University, 4825 Mount Royal Gate SW, Calgary, Canada T3E6K6. uzokafm@yahoo.com
Obot
Okure
O
Barker
Ken
K
Osuji
J
J
D000465:Algorithms; D020000:Decision Support Systems, Clinical / Q000295:instrumentation*; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D017143:Fuzzy Logic; D006801:Humans; D008288:Malaria / Q000175:diagnosis*; D008960:Models, Psychological; D013223:Statistics as Topic