Diabetes Prediction Using Machine Learning: A Comparative Analyses of Classification Algorithms

Diabetes is a chronic metabolic condition identified by increased levels of sugar in the blood. It is also commonly referred to as diabetes mellitus. Either inadequate insulin production or inadequate insulin use by the organism cause this condition. Early identification and prediction of the onset of diabetes can lead to timely interventions and improved patient outcomes. In an effort to increase the precision and effectiveness of machine learning predictive models, this research article provides a thorough analysis of diabetes prediction utilizing these techniques. The study utilizes a diverse dataset comprising of 768 observations, lifestyle factors, and clinical indicators obtained from a cohort of individuals with and without diabetes. Feature selection methods are employed to identify the most relevant variables, ensuring a robust and parsimonious model. Various machine learning algorithms are implemented and compared to determine the optimal predictive model.