A Data-Driven Diabetes Predictive Model Using a Novel Optimized Weighted Ensemble Approach

Early detection of diabetes plays a crucial role in improving health outcomes and can help people avoid harmful diabetes complications. Machine learning algorithms are being used to diagnose a disease in its early stages. This study proposes an optimized weighted ensemble model that can predict the risk of type 2 diabetes mellitus. A diabetes dataset of 403 patients from the Department of Medicine at the University of Virginia given by Dr. John Schorling has been used. We assessed ridge regressor, LASSO, feedforward artificial neural networks, and linear regression prediction performance. These models were then combined to create an optimized weighted ensemble model for prediction. We evaluated our prediction models using standard performance metrics: coefficient of determination (R2 score), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The results showed that the proposed optimized weighted ensemble model outperformed individual models, achieving the highest 0.81 (R2 score) and lowest 0.98 (MSE).