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.
@article{arxiv.2506.13786,
title = {Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction},
author = {Vuong M. Ngo and Tran Quang Vinh and Patricia Kearney and Mark Roantree},
journal= {arXiv preprint arXiv:2506.13786},
year = {2025}
}
Comments
17th International Conference on Computational Collective Intelligence, LNAI, Springer, 11 pages