Modified mixture of experts for diabetes diagnosis.
Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of diabetics and subjects having risk factors of diabetes. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and their performances in detection of diabetics were compared. The performance of the classification algorithms was illustrated on the Pima Indians diabetes data set. The present research demonstrated that the modified mixture of experts (MME) achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.
Ubeyli
Elif Derya
ED
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Sögütözü, Ankara, Turkey. edubeyli@etu.edu.tr
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003920:Diabetes Mellitus / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007198:Indians, North American; D016013:Likelihood Functions; D016571:Neural Networks, Computer; D012307:Risk Factors; D012680:Sensitivity and Specificity
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