A Machine Learning Approach for Prediction of Diabetes Mellitus

Abstract

Diabetes Mellitus is among chronic diseases and lots of people are suffering with this disease. It may cause many complications and have a high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. There is no doubt that this alarming figure needs great attention. With the rapid development of Machine Learning, machine learning has been applied to many aspects of medical health. There are several Machine learning algorithms that are used to perform predictive analysis in various fields. Predictive analysis in healthcare is a challenging task but ultimately can help practitioners make data informed about a patient's health and treatment. In this project, for experiment purposes, we have taken a dataset which is originally from the National Institute of diabetes and digestive and kidney diseases. All patients here are females at least 21 years old of Pima Indian heritage. By studying the dataset, we must find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. The objective of this project is to diagnostically predict whether the patient has diabetes or not, based on certain diagnostic measurements included in the dataset. We have proposed a diabetes prediction model for better classification of diabetes by applying some popular machine learning algorithms namely, Logistic Regression, Random Forest Algorithm and KNN Algorithm to predict Diabetes.