A critical review of machine learning approaches for predicting diabetes mellitus

Abstract

Diabetes is a complex, chronic metabolic condition that progresses gradually and causes the patient to have increased blood glucose levels. The three most prevalent kinds of diabetes are type 1, type 2, and gestational. It results in serious health problems such renal problems, heart attacks, foot ulcers, and visual impairment. One of the primary symptoms is hyperglycemia. Early illness progression detection aids in the reduction of additional consequences. The classification of quantitative data most typically uses machine learning techniques. It makes it easier to pinpoint the important elements that are most responsible for the development of diseases. During the pre-processing stage, the least important properties are removed. The classification or forecasting of the condition Diabetes Mellitus using conventional. The discussion and analysis of supervised machine learning techniques focused on their predictive accuracy. The most popular and effective machine learning model among the several extant algorithms is examined using a variety of tools, datasets, and measures. One of the most effective machine learning models is the Deep learning algorithm of artificial neural networks, which is favored by many scientists and researchers in the field of health.