A NOVEL EARLY PREDICTION OF DIABETES MELLITUS SCREENING FRAMEWORK WITH ADVANCED DEEP LEARNING TECHNIQUES

Abstract

Diabetes Mellitus is a chronic and life-threatening disease still posing a significant burden on the health system worldwide. During recent years, there has been a surge in the development of machine learning (ML) models for predicting the risk of developing diabetes, although many of these models are considered as “black boxes,” that is, they are primarily concerned with prediction accuracy, and provide limited information about what is driving the decisions of their models. The lack of transparency reduces their usefulness in real world clinical situations, where understanding of the causes and risk factors is pivotal for optimal prevention and treatment. In an effort to overcome this limitation, the present study proposes Ensemble-based machine learning framework that enhances the prediction accuracy and specifies the most significant factors that play a crucial role in the onset of diabetes. A dataset with both clinical and demographic data was used to train and test multiple models including XGBoost, Random Forest, Support Vector Machines. The ensemble model proposed in this work showed an accuracy of 87% and good precision compared to some well-known models. The study focuses not only on the prediction but also the interpretability using SHapley Additive exPlanations (SHAP) values. Through this analysis, key predictors were identified and glucose, Body Mass Index (BMI), and age were identified as the most influential. These findings give a better idea of the effects of various factors on diabetes risk. Furthermore, the research results can help health care professionals in their decision making processes based on the facts. Early detection allows for more effective targeted prevention strategies. This can help to enhance the quality of healthcare for patients and optimize its resource use. Furthermore, the framework illustrates the use of accuracy and interpretability to foster trust in systems with AI. The methodology may be applied to other chronic diseases as well, making it a valuable contribution to the field of Intelligent Healthcare.