A Diabetic Prediction System Based on Mean Shift Clustering

Abstract

An abnormal rise in glucose levels may lead to diabetes. Around 30 million people are diagnosed with this disease in our country. In this perspective Indian Council of Medical Research funded by Registry of People with diabetes in India have taken an initiative and come up with numerous solutions but unfortunately neither of them has taken shape. Initially, the behavior of chemical reaction between glucose with chemical agent is estimated and tracked in the region of interest via mean shift algorithm using spatial and range information. This color change is related to plasma glucose concentration (plas), diastolic blood pressure, (pres.) Triceps skin fold thickness(skin), 2_hour serum insulin(insu), Body mass index and age. These features obtained from these 768 instances are classified using Naïve Bayes Algorithm. The results are compared with our previous work, an integrated system of K means and Naïve Bayes approach in terms of sensitivity, specificity, precision, and F-measure. It is worth noticing that our integration of mean-shift clustering and classification gives promising results with an utmost accuracy rate of 99.42% even after removing nearby duplicates in predefined clusters.