Implementing a Model to Detect Diabetes Prediction using Machine Learning Classifiers

Abstract

Diabetics is a metabolic disease caused because of high glucose level in a body. It may cause many complications like kidney damage, heart related problems, eye problem, blood pressure and it can affect other organs of human body. Now a days there is an increase in number of people suffering from diabetics. Diabetics can be treated if it is predicted earliest stage. In this approach various machine learning techniques can be used from predicting diabetics with higher accuracy. To achieve the goal, we will use different ML and ensemble techniques which are K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Ada Boost, Naive Bayie and XG Boost. Our Result shows that XG Boost achieved higher accuracy compared to other machine learning techniques