Diabetes Mellitus Prediction using Different Ensemble Machine Learning Approaches

Abstract

Nowadays Diabetes Mellitus is one of the most rapidly growing diseases which makes the biggest contribution to morbidity and mortality worldwide. Diabetes Mellitus is a group of metabolic disorders defined by high blood glucose level over a prolonged period. Although this disease is familiar as hereditary disease, many people are suffering from this disease without having family background. If diabetes is not in control, the level of glucose goes up and it may cause damage to small vessels in human body which appears most often in the nerves, feet, eyes even in heart and kidneys. To get rid of these issues, it is very crucial to predict diabetes on the early stage. Hence, we have decided to do research on diabetes prediction using Machine Learning algorithms. In this study, we have used three popular Machine Learning algorithms called AdaBoost, Bagging and Random Forest. To train and test the algorithms we have collected real time information of both diabetic and non-diabetic people. The dataset contains 464 instances with 22 unique risk factors. In between the three algorithms, AdaBoost gave 97.84% accuracy, Bagging gave 98.28% accuracy and Random Forest gave 99.35 % accuracy with respect to predict diabetes disease precisely.