(untitled) (International Journal of Food and Nutritional Sciences, 2021)
The contemporary world finds itself grappling with the pervasive impact of diseases, and diabetes stands at the forefront. As per the “International Diabetes Federation”, a staggering 246 million individuals worldwide currently live with diabetes, and this figure is projected to soar to a monumental 380 million by the year 2025. This metabolic disorder, characterized by the mismanagement of blood glucose levels, engenders a heightened susceptibility to an array of ailments, including heart attacks, kidney disease, and renal failure. In light of these concerns, healthcare practitioners necessitate a dependable prognostic methodology to effectively diagnose diabetes mellitus. Fortunately, the rapid strides made in the realm of Machine Learning and Data Mining present a plethora of techniques and algorithms within the domain of artificial intelligence that can be harnessed with efficacy for disease prediction and diagnosis. This comprehensive paper endeavors to furnish a discerning review of the machine learning and data mining methods routinely employed in the analysis and prognostication of diabetes.