An Empirical Study on Diabetes Mellitus Prediction for Typical and Non-Typical Cases using Machine Learning Approaches

Diabetes is a non-communicable disease and increasing at an alarming rate all over the world. Having a high sugar level in blood or lack of insulin are the primary reasons. So, it is important to find an effective way to predict diabetes before it turns into a major problem for human health. It is possible to take control of diabetes on an early stage if we take precautions. For this study, we have collected 340 instances with 26 features of patients who have already diabetes with various symptoms categorized by two types Typical and Non-Typical. For training the dataset, cross-validation technique has been used and for classification, three Machine Learning (ML) algorithms such as Bagging, Logistic Regression and Random Forest have been used. The accuracy for Bagging 89.12%, for Logistic Regression 83.24% and for Random Forest 90.29% which are very appreciative.