Diabetic Prediction with Ensemble Model and Feature Selection Using Information Gain Method

Diabetes is a chronic medical condition characterized by high levels of blood glucose levels, there is no specific cure for it, there are only ways to control it. Predicting diabetes in its early stages is very crucial as it affects the overall health of the patients when not predicted early. Through this work an Ensemble method of Logistic Regression, SVM, Naive Bayes and Random Forest Classifiers has been implemented. The result obtained has thoroughly been analyzed and documented. Proposed ensemble model obtained an accuracy of 85%.