Diabetes is one of the most awful diseases in the world which has no method to treat it after a set stage. Over 422 million individuals in the world are diagnosed with diabetes and many more are at danger. Thus, timely detection and medication is crucial to reduce diabetes and its accompanying health consequences. In this study a system is developed for diabetes diseases prediction and classification using Machine Learning (ML) approaches. The dataset is taken from KM Hospital and Research Centre, Pune, Sahyadri Hospital Pune and Research Centre and Data. Four independent ML algorithms Logistic Regression, Naïve Bayes, Support Vector Machine and Decision Tree are employed and analyzed the model using multiple quantitative criteria. The purpose of this framework is to discover diabetes early and to save money and time of a patient using several machine learning methods.

Diabetes, Prediction, Classification, Machine Learning, Model Evaluation, Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree.