Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction

2025/06/11 by Vuong M. Ngo, Ngo, Vuong M., Vinh, Tran Quang +4 · 1 citation
Health Professions · Computer Science · #Artificial Intelligence in Healthcare #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2506.13786

Abstract

Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels, leading to complications like heart disease, kidney failure, and nerve damage. Accurate state-level predictions are vital for effective healthcare planning and targeted interventions, but in many cases, data for necessary analyses are incomplete. This study begins with a data engineering process to integrate diabetes-related datasets from 2011 to 2021 to create a comprehensive feature set. We then introduce an enhanced bagging ensemble regression model (EBMBag+) for time series forecasting to predict diabetes prevalence across U.S. cities. Several baseline models, including SVMReg, BDTree, LSBoost, NN, LSTM, and ERMBag, were evaluated for comparison with our EBMBag+ algorithm. The experimental results demonstrate that EBMBag+ achieved the best performance, with an MAE of 0.41, RMSE of 0.53, MAPE of 4.01, and an R2 of 0.9.

Citations

• Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
• The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
• Greedy function approximation: A gradient boosting machine.

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