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A Hybrid Ensemble Approach for Early‐Stage Diabetes Detection
Healthcare Technology Letters, Volume: 13, Issue: 1
Swansea University Authors:
RACHANA KC, Scott Yang
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DOI (Published version): 10.1049/htl2.70060
Abstract
Diabetes has become a critical global health concern, particularly in regions where access to diagnostic facilities is limited. In this work, we propose a hybrid framework that combines extreme gradient boosting (XGBoost) and deep neural networks (DNNs) for early-stage diabetes detection, using soft...
| Published in: | Healthcare Technology Letters |
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| ISSN: | 2053-3713 2053-3713 |
| Published: |
Institution of Engineering and Technology (IET)
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71382 |
| Abstract: |
Diabetes has become a critical global health concern, particularly in regions where access to diagnostic facilities is limited. In this work, we propose a hybrid framework that combines extreme gradient boosting (XGBoost) and deep neural networks (DNNs) for early-stage diabetes detection, using soft voting to generate the final ensemble predictions. The proposed framework was evaluated on two datasets: the widely used Diabetes UCI dataset and a newly collected dataset from Nepal. The ensemble method achieved 99% accuracy (ACC) with an area under the curve (AUC) of 1.00 on the Diabetes UCI dataset, and 91% ACC with a 0.96 AUC on the Nepal diabetes dataset, demonstrating its strong generalisability across distinct populations. Compared to individual models, the hybrid approach offered increased stability and a lower rate of false negatives, which is particularly important in clinical contexts. These findings highlight the potential of hybrid machine learning–deep learning models as cost-effective, scalable and generalisable decision-support tools for diabetes risk assessment. |
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| Item Description: |
Letter |
| College: |
Faculty of Science and Engineering |
| Funders: |
Swansea University |
| Issue: |
1 |