Fuzzy based expert system for diabetes diagnosis and insulin dosage control

The paper presents a fuzzy based expert system to handle the dynamics of diabetes diagnosis and its medication. Imprecise, vague and uncertain information is handled effectively to diagnose the Type-1 diabetes in an individual. Parameters like body mass index (bmi), plasma glucose level, minimum blood pressure and serum insulin level are used for the diagnosis based on the fuzzy logic, and the severity or probability of Type-1 diabetes is calculated. An insulin dosage (units/kg/day) is recommended by considering two attributes, i.e., plasma glucose level and bmi. Different levels of plasma glucose are captured on a daily basis to compute required dosage levels. Fuzzy logic tends to accurately calculate the probability to avoid any hypoglycemic condition in a diabetic. The final output is semantically arranged which depicts different parameters in terms of the fuzzy numbers like low, medium or high, and the probability of diagnosis in terms of five such fuzzy numbers like very low, low, medium, high or very high. This expert system can be effectively used for Type-1 diabetes diagnosis.