Type 2 Diabetes Mellitus: Early Detection using Machine Learning Classification

Abstract

Type 2 Diabetes Mellitus (T2DM) is a growing global health problem that significantly impacts patient's quality of life and longevity. Early detection of T2DM is crucial in preventing or delaying the onset of its associated complications. This study aims to evaluate the use of machine learning algorithms for the early detection of T2DM. A classification model is developed using a dataset of patients diagnosed with T2DM and healthy controls, incorporating feature selection techniques. The model will be trained and tested on machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machines. The results showed that the Random Forest algorithm achieved the highest accuracy in detecting T2DM, with an accuracy of 98%. This high accuracy rate highlights the potential of machine learning algorithms in early T2DM detection and the importance of incorporating such methods in the clinical decisionmaking process. The findings of this study will contribute to the development of a more efficient precision medicine screening process for T2DM that can help healthcare providers detect the disease at its earliest stages, leading to improved patient outcomes.