Prediction of Diabetes Gene data Using Machine Learning Techniques

Abstract

Diabetes complications significantly affect patients' quality of life. Diabetes and the health problems associated with it can be prevented by early diagnosis and treatment. The purpose of this study is to determine the risk of diabetes among those who need treatment to prevent diabetes and its associated health problems. According to their lifestyle and family background, this study evaluates the risk of diabetes among individuals. Type 2 Diabetes (T2D) is often detected too late in its clinical course with many patients presenting with complications of unrecognised T2D at the time of diagnosis. This paper provides an overview of several supervised machine learning omics T2D gene categorization methods important theoretical question, pointing the researcher in fascinating new paths for study, and recommending possible combinations of biases that haven't been investigated yet.