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

arXiv: https://arxiv.org/abs/2506.13786 · HTML: https://mlsanity.com/paper/2506.13786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)

## Built on by (most cited first, 1 of 1)

- [Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland](https://mlsanity.com/paper/2511.03749/) (2025)

## Abstract (arXiv)

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.