Comparative Analysis of Machine Learning-Based Diabetes Prediction Approaches

Abstract

The early prediction of diabetes mellitus may help improve the health of patients and cure them of this disease. In recent years, machine learning techniques have been widely used to predict diabetes in its early stages. In this chapter, an attempt has been made to analyse the performance of different machine learning techniques for diabetic prediction. Four well-known machine learning techniques, named as random forest, support vector machine, decision tree, and XGBoost are used. These techniques are evaluated on the Indian Diabetes dataset. Experimental results reveal that random forest algorithm achieved highest accuracy than the other techniques in terms of performance measures. These techniques will help to reduce diabetes incidence and health care costs. This work can be used to envisage diabetes in its early stages.