Comparision Study Of Classification Techniques For Diabetes Prediction

Abstract

Diabetes is one of the deadliest diseases in the world. It is not only a disease but also a creator of different kinds of diseases. With advances in technology, machine learning helps to predict the occurrence of diabetes in a subject. But with new algorithms and methodologies being developed every day, it becomes difficult to choose one. We carry out a comparison study of classification algorithms that are widely used for prediction and establish which algorithm gives more accurate results. The aim of this analysis is to develop a system which might predict the diabetic risk level of a patient with a better accuracy. Model development is based on categorization methods as Decision Tree, ANN, Naive Bayes and SVM algorithms. For Decision Tree, the models give precisions of 85%, for Naive Bayes 77% and 77.3% for Support Vector Machine. Outcomes show a significant accuracy of the methods.