Diabetes Prediction and Classification using Machine Learning Algorithms
Yogita Dubey, Pushkar Wankhede, Tanvi Borkar, Amey Borkar, Kajal Mitra
Abstract
Diabetes is one of the most grievous diseases in the world which has no remedy to cure it after a particular stage. Over 422 million people in the world are diagnosed with diabetes and many others are at jeopardy. Thus, timely diagnosis and medication is required to inhibit diabetes and its associated health problems. In this paper a framework is proposed for diabetes diseases prediction and classification using Machine Learning (ML) algorithms. The dataset is collected from Shalinitai Meghe Hospital and Research Centre, Nagpur, NKP Salve Institute of Medical Sciences and Research Centre and Mendeley Data. Four different ML algorithms Logistic Regression, Naive Bayes, Support Vector Machine and Random Forest are applied and evaluated the model with various quantitative measures. The motive of this framework is to diagnose diabetes early and to save money and time of a patient using various machine learning approaches.