A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk

Abstract

The application of Arti cial intelligence (AI) has become a valuable part of medical research. These days diabetes is one of the top maladies on the planet. Nowadays it has become a common disease and alarming as people are living in polluted areas and eating unhygienic foods. People with diabetes are probably going to pass on at a more youthful age than individuals who don't have diabetes. We hope this study could be very helpful in medical science to predict the risk score of type II Diabetes Mellitus (DM). Our model consists of four machine learning algorithms which are- K-Nearest Neighbor, Random forest, Decision tree and Logistic Regression. These algorithms have been applied on a dataset containing 15000 type 2 diabetes patients along with eight features that describe the state of patients such as glucose, BMI, age, pregnancy, blood pressure (BP), Diabetes Pedigree Function, Skin thickness and insulin. Moreover, one deep learning algorithm called CNN has been used. All of the ve algorithms have been used on the dataset and the Random forest gives the best accuracy of almost 92.60 percent where other algorithms give less accuracy.