A Review of Machine Learning in Diabetes Research

: Diabetes, due to its increasing prevalence, poses an ever-growing challenge to public health. Effective management of diabetes requires accurate prediction, timely diagnosis, and personalized treatment strategies. Recent advancements in machine learning (ML) have revolutionized diabetes research by offering innovative solutions in these areas. This review provides an overview of ML techniques applied in diabetes research, with a focus on learning methods. Supervised learning methods have played a key role in diabetes classification and risk prediction. Unsupervised learning techniques are used for patient segmentation and finding key risk factors. Deep learning models have showed exceptional performance in jobs such as diabetic retinopathy detection and blood glucose level prediction. This review examines the strengths and limitations of these models, evaluates their performance, and discusses the challenges they face in clinical implementation, such as data quality issues, model interpretability, and computational demands. Finally, future directions highlight the potential of integrating big data and deep learning to enhance the accuracy, efficiency, and scalability of machine learning in diabetes management.