Comparision Study Of Classification Techniques For Diabetes Prediction

M. Madhavi, G. Vinoothna, D. Rushalika, B. Sai Kiran


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.

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Copyright (c) 2020 M. Madhavi, G. Vinoothna, D. Rushalika, B. Sai Kiran

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