Exploring the effectiveness of machine learning algorithms for early detection of Type-2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) poses a significant global health challenge, greatly impacting the well-being and longevity of individuals. Detecting T2DM early on is of utmost importance as it prevents or delays associated complications. This research study seeks to assess the effectiveness of employing ma-chine learning algorithms for the early identification of T2DM. A classification model is constructed utilizing a dataset comprising T2DM-diagnosed patients and healthy controls, incorporating advanced feature selection techniques. The model is subjected to rigorous training and testing, utilizing machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machines. The results demonstrate that the Random Forest algorithm achieves an impressive accuracy of 98 % in detecting T2DM. This remarkable accuracy underscores the immense potential of machine learning algorithms in facilitating the early detection of T2DM. It emphasizes the significance of integrating these methods into the clinical decision-making process. The findings from this study will significantly contribute to developing an enhanced precision medicine screening process for T2DM, empowering healthcare providers to identify the disease at its nascent stages, thereby leading to improved patient outcomes.