Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms
Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons (PHs) based on historical BG values colle...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7902418 |
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| author | Dae-Yeon Kim Dong-Sik Choi Ah Reum Kang Jiyoung Woo Yechan Han Sung Wan Chun Jaeyun Kim |
| author_facet | Dae-Yeon Kim Dong-Sik Choi Ah Reum Kang Jiyoung Woo Yechan Han Sung Wan Chun Jaeyun Kim |
| author_sort | Dae-Yeon Kim |
| collection | DOAJ |
| description | Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons (PHs) based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information (times) as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm (GA) was applied to the five prediction models to optimize their weights through ensemble techniques and to yield (output) the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average (ARIMA) model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error (RMSE) and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08 (±3.19), 19.25 (±5.28), and 31.30 (±8.81) mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28 (±3.34), 19.99 (±5.59), and 33.13 (±9.27) mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance. |
| format | Article |
| id | doaj-art-ceffed557904482aa3df79c15a00a009 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ceffed557904482aa3df79c15a00a0092025-02-03T01:01:21ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7902418Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic AlgorithmsDae-Yeon Kim0Dong-Sik Choi1Ah Reum Kang2Jiyoung Woo3Yechan Han4Sung Wan Chun5Jaeyun Kim6Department of Internal MedicineDepartment of Medical ScienceDepartment of Information SecurityDepartment of Big Data EngineeringDepartment of Medical ScienceDepartment of Internal MedicineDepartment of Big Data EngineeringForecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons (PHs) based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information (times) as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm (GA) was applied to the five prediction models to optimize their weights through ensemble techniques and to yield (output) the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average (ARIMA) model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error (RMSE) and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08 (±3.19), 19.25 (±5.28), and 31.30 (±8.81) mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28 (±3.34), 19.99 (±5.59), and 33.13 (±9.27) mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance.http://dx.doi.org/10.1155/2022/7902418 |
| spellingShingle | Dae-Yeon Kim Dong-Sik Choi Ah Reum Kang Jiyoung Woo Yechan Han Sung Wan Chun Jaeyun Kim Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms Complexity |
| title | Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms |
| title_full | Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms |
| title_fullStr | Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms |
| title_full_unstemmed | Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms |
| title_short | Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms |
| title_sort | intelligent ensemble deep learning system for blood glucose prediction using genetic algorithms |
| url | http://dx.doi.org/10.1155/2022/7902418 |
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