Early Phase Detection of Diabetes Mellitus Using Machine Learning

In current scenario of healthcare, Diabetes Mellitus stands as an incurable condition, underscoring the imperative of early detection. Factors contributing to the onset of diabetes encompass aging, weight gain, sedentary lifestyle, genetic predisposition, poor nutrition, irregular routines, elevated cholesterol levels, and other associated conditions. The intersection of healthcare and machine learning unveils intriguing possibilities, capturing the attention of medical professionals. This study aspires to empower healthcare practitioners in predicting diabetes at an early stage through the application of machine learning techniques. By scrutinizing and comparing classification algorithms, including Random Forest, Supervised Machine Learning, and Decision Tree, we sought to discern their efficacy in forecasting diabetes mellitus. A systematic evaluation identified a model achieving an impressive accuracy rate of 98.56%, offering a substantial contribution to the utilization of machine learning for diabetes prediction. This research augments our understanding of the practical implications of machine learning in the healthcare domain, particularly in the context of early disease detection.