Isaac Mumo and Dr. Mvurya Mgala

Abstract

Type 2 Diabetes Mellitus is a prevalent chronic disease characterized by insulin resistance and hyperglycemia. Early detection and intervention are crucial for preventing complications and improving patient outcomes. Although machine learning models hold the potential to enhance early detection and support proactive interventions for Type 2 Diabetes Mellitus, previous studies have shown that this area remains largely unexploited especially in the developing countries. The aim of this study is to systematically review relevant literature on the application of machine learning techniques for predicting Type 2 Diabetes Mellitus. The methodology for this literature review will involve formulation of research questions and objectives for the review, researching the extant scholarly literature from journals and relevant articles, reading and assessing the quality of the primary studies, analyzing and summarizing the data. The findings from the review of literature will inform the development of a machine learning model for predicting Type 2 Diabetes Mellitus. The early prediction will benefit healthcare professionals and public health experts in managing type 2 Diabetes Mellitus by initiating early intervention programmes.

Keywords: Machine Learning; Modeling; Type 2 Diabetes Mellitus; Prediction