Optimizing Type-2 Diabetes Mellitus Early Detection using ML Techniques

A major global health concern, T2DM (type 2 diabetes mellitus) has a substantial impact on people's longevity and general well-being. In order to avoid or postpone difficulties, early detection is essential. To diagnose type 2 diabetes at an earlier stage, the usefulness of machine learning techniques is assessed in this work. Using a dataset that included both healthy controls and patients having type 2 diabetes, a classification model was created, utilising sophisticated feature selection approaches to improve performance. K-Nearest Neighbours, support vector machines, random forests, logistic regression, and decision trees are just a few of the machine learning algorithms that have undergone extensive training and testing. Of these, the Random Forest method demonstrated the promise of machine learning in T2DM detection with an exceptional 98% accuracy rate. The study emphasises how crucial it is to include these cutting-edge techniques into clinical decision-making procedures. In order to improve patient outcomes and lessen the disease's worldwide impact, the findings help build precision medicine screening technologies that allow healthcare practitioners to identify type 2 diabetes in its earliest stages.