A Survey on Predicting Type 2 Diabetes Using Machine Learning Classification Algorithm

Abstract

Type 2 diabetes has the potential to become humanity's most severe plague. If all diabetics in the world came together, it could shape the world's third-largest country. Diabetes has recently increased its prevalence by 13% in India. While diet and a change in lifestyle are the cornerstones of type 2 diabetes treatment, the majority of patients ultimately need medicine to stabilize glucose and associated health concerns. As a result, early detection and recovery are critical to avoid future complications. This research uses the gradient boosting algorithm a well known classification machine learning algorithm to estimate the likelihood of type 2 diabetes in humans. The purpose of this project is to enhance estimates such that the logistic regression technique may be used on any dataset with dependable results. This study we use the dataset called Pima Diabetes for Indian dataset which was processed and visualized by using Python. Our model performs admirably, with a prediction accuracy of 77.05 percent. This study will assist future researchers in the development of new diabetes prevention techniques.

Keywords: Diabetes, logistic regression, gradient boosting,