Advancements in machine learning techniques for Diabetes Mellitus: a review of progression, challenges and future directions

Diabetes Mellitus (DM) is a long-term metabolic disease marked by high blood sugar that can cause problems that impact several organ systems. With millions of affected individuals globally, diabetes poses a serious challenge to the administration of healthcare. The dynamic character of DM combined with the interaction of hereditary tendency. Precisely forecasting the progression of diabetes mellitus is essential for tailored patient treatment and successful intervention techniques. Finding patterns that influence the course of an illness and analyzing a variety of data sources and impact of risk factors are made possible by machine learning. Through an analysis of various datasets, genetic, and lifestyle factors, this paper aims to explore the application of machine learning techniques in predicting the progression of diabetes mellitus across various datasets. Specifically, it will explore the evolution of ML models, datasets, feature selection methods, risk factors impact, and their application in predicting diabetes onset, risk assessment, glucose monitoring, and personalized treatment recommendations. The research will look at how various algorithms may be used to predict the progression of diabetes, which might help healthcare personnel with early intervention and customized treatment regimens. It examines how machine learning techniques have developed, how well they are now able to diagnose, predict, and treat diabetes, as well as the difficulties that lie ahead and potential paths for better patient care and disease management.