Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data
Abstract Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, includi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-11-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00362-y |
| _version_ | 1797414063565701120 |
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| author | Ashir Javeed Johan Sanmartin Berglund Ana Luiza Dallora Muhammad Asim Saleem Peter Anderberg |
| author_facet | Ashir Javeed Johan Sanmartin Berglund Ana Luiza Dallora Muhammad Asim Saleem Peter Anderberg |
| author_sort | Ashir Javeed |
| collection | DOAJ |
| description | Abstract Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. |
| first_indexed | 2024-03-09T05:27:39Z |
| format | Article |
| id | doaj.art-b93007edce1540cf96d5b8648d2c9595 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-09T05:27:39Z |
| publishDate | 2023-11-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-b93007edce1540cf96d5b8648d2c95952023-12-03T12:35:45ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-11-0116111410.1007/s44196-023-00362-yPredictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health DataAshir Javeed0Johan Sanmartin Berglund1Ana Luiza Dallora2Muhammad Asim Saleem3Peter Anderberg4Aging Research Center, Karolinska InstitutetDepartment of Health, Blekinge Institute of TechnologyDepartment of Health, Blekinge Institute of TechnologyCenter of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Chulalongkorn UniversityDepartment of Health, Blekinge Institute of TechnologyAbstract Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors.https://doi.org/10.1007/s44196-023-00362-ySleep apneaDeep learningComputer visionFeature engineering |
| spellingShingle | Ashir Javeed Johan Sanmartin Berglund Ana Luiza Dallora Muhammad Asim Saleem Peter Anderberg Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data International Journal of Computational Intelligence Systems Sleep apnea Deep learning Computer vision Feature engineering |
| title | Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data |
| title_full | Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data |
| title_fullStr | Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data |
| title_full_unstemmed | Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data |
| title_short | Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data |
| title_sort | predictive power of xgboost bilstm model a machine learning approach for accurate sleep apnea detection using electronic health data |
| topic | Sleep apnea Deep learning Computer vision Feature engineering |
| url | https://doi.org/10.1007/s44196-023-00362-y |
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