Khosro Rezaee - Biomedical Engineering Student, Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Javad Haddadnia - Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University of Sabzevar, Sabzevar, Iran.

Objective: International Diabetes Organization estimates that there are ۲۸۵ million people worldwide who suffer from diabetes, and this figure is expected to increase to ۴۵۰ million in next ۲۰ years. According to statistics issued by the World Health Organization, diabetes is considered among ten leading causes of death in world and its prevalence in the population is growing.This paper deals with designing and building an Expert System for Diabetes Mellitus diagnosis. Materials and Methods: We randomly select ۷۸ knowingly volunteered patients as non-intervention from approximately ۱۷ families in Tovhid town in Sabzevar city to test system hardware. The output of these information and ADA database was used to test the performance of software part of the proposed system. In this system, at first citizen information through a wireless sensor network (WSN) is received and these data is transmitted to the central data processing system (CDPS). In the CDPS, intelligent software uses SVM technique based on ۸ features to classify data and warns diabetes person due statistical changes. Results: Acceptable level of accuracy of the proposed system with ۹۵.۰۲%±۱.۲۴۵%, sensitivity ۹۸.۳۰±۰.۸۵% and specificity of ۹۷.۵۲±۱.۰۶% and Kappa coefficient equal to ۰.۹۵ is optimal performance Conclusion: Accuracy and high speed in data classification make the exact output of the software which is available online information so specialist will be able to alert suspect patients or identity diabetes patients without referring them to therapeutic centers.

Diabetes, Support vector machine, Online diabetic data (ODD), Wireless sensor network, Central Data processing system.