Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach
Introduction: The diabetes pandemic, including 828 million adults worldwide in 2022, would benefit from continued development of novel, effective and accurate blood glucose prediction systems. Using the DiaTrend dataset, this study used stacking machine learning optimized by Grey Wolf Optimizer to c...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Endocrine and Metabolic Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666396125000391 |
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| author | Vincent B. Liu Laura Y. Sue Oscar Madrid Padilla Yingnian Wu |
| author_facet | Vincent B. Liu Laura Y. Sue Oscar Madrid Padilla Yingnian Wu |
| author_sort | Vincent B. Liu |
| collection | DOAJ |
| description | Introduction: The diabetes pandemic, including 828 million adults worldwide in 2022, would benefit from continued development of novel, effective and accurate blood glucose prediction systems. Using the DiaTrend dataset, this study used stacking machine learning optimized by Grey Wolf Optimizer to construct and assess prediction models for blood glucose levels in type 1 diabetes patients. Methods: The DiaTrend dataset includes 27,561 days of continuous glucose monitoring and 8220 days of insulin pump data for 54 patients with type 1 diabetes. Grey Wolf optimization was used to tune and evaluate three machine learning algorithms – Random Forest, LSTM, GRU – for blood glucose predictions, whose predictions were then combined into an XGBoost stacking ensemble meta-learner. Results: This study looked at three baseline algorithms for predicting blood glucose levels. Machine learning models Random Forest, LSTM, and GRU served as baselines, with MAE, RMSE, and MARD values. GRU had the best predictive accuracy of the initial models. Grey Wolf optimization contributed to achieving optimal baseline model results. Stacking ensemble learning via XGBoost meta-learner (MAE = 10.65, RMSE = 14.59, MARD = 6.98) achieved higher performance than the baseline models. Conclusion: The GRU method with Grey Wolf optimization outperformed the other models with the lowest MAE, RMSE, and MARD, but the Stacked XGBoost model fared best. These findings emphasize the need to improve parameter selection with approaches such as Grey Wolf or stacking ensemble methods to achieve accurate blood glucose predictions. These prediction models can aid in the continued development of monitoring devices, and algorithms for these devices, which contain alert systems for impending abnormal blood glucose levels, allowing for timely diabetes self-management. |
| format | Article |
| id | doaj-art-855423b62843452987ee2b4ac4785f1a |
| institution | OA Journals |
| issn | 2666-3961 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Endocrine and Metabolic Science |
| spelling | doaj-art-855423b62843452987ee2b4ac4785f1a2025-08-20T01:59:13ZengElsevierEndocrine and Metabolic Science2666-39612025-06-011810025310.1016/j.endmts.2025.100253Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approachVincent B. Liu0Laura Y. Sue1Oscar Madrid Padilla2Yingnian Wu3Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, United States of America; Corresponding author at: 8125 Math Sciences Bldg, Los Angeles, CA 90095, United States of America.Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, United States of AmericaDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, United States of AmericaDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, United States of AmericaIntroduction: The diabetes pandemic, including 828 million adults worldwide in 2022, would benefit from continued development of novel, effective and accurate blood glucose prediction systems. Using the DiaTrend dataset, this study used stacking machine learning optimized by Grey Wolf Optimizer to construct and assess prediction models for blood glucose levels in type 1 diabetes patients. Methods: The DiaTrend dataset includes 27,561 days of continuous glucose monitoring and 8220 days of insulin pump data for 54 patients with type 1 diabetes. Grey Wolf optimization was used to tune and evaluate three machine learning algorithms – Random Forest, LSTM, GRU – for blood glucose predictions, whose predictions were then combined into an XGBoost stacking ensemble meta-learner. Results: This study looked at three baseline algorithms for predicting blood glucose levels. Machine learning models Random Forest, LSTM, and GRU served as baselines, with MAE, RMSE, and MARD values. GRU had the best predictive accuracy of the initial models. Grey Wolf optimization contributed to achieving optimal baseline model results. Stacking ensemble learning via XGBoost meta-learner (MAE = 10.65, RMSE = 14.59, MARD = 6.98) achieved higher performance than the baseline models. Conclusion: The GRU method with Grey Wolf optimization outperformed the other models with the lowest MAE, RMSE, and MARD, but the Stacked XGBoost model fared best. These findings emphasize the need to improve parameter selection with approaches such as Grey Wolf or stacking ensemble methods to achieve accurate blood glucose predictions. These prediction models can aid in the continued development of monitoring devices, and algorithms for these devices, which contain alert systems for impending abnormal blood glucose levels, allowing for timely diabetes self-management.http://www.sciencedirect.com/science/article/pii/S2666396125000391DiabetesPredictionMachine learningStackingGrey wolfTime series |
| spellingShingle | Vincent B. Liu Laura Y. Sue Oscar Madrid Padilla Yingnian Wu Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach Endocrine and Metabolic Science Diabetes Prediction Machine learning Stacking Grey wolf Time series |
| title | Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach |
| title_full | Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach |
| title_fullStr | Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach |
| title_full_unstemmed | Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach |
| title_short | Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach |
| title_sort | optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach |
| topic | Diabetes Prediction Machine learning Stacking Grey wolf Time series |
| url | http://www.sciencedirect.com/science/article/pii/S2666396125000391 |
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