Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques
Sleep disorder detection has greatly improved with the integration of machine learning, offering enhanced accuracy and effectiveness. However, the labor-intensive nature of diagnosis still presents challenges. To address these, we propose a novel coordination model aimed at improving detection accur...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1506770/full |
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| author | Muhammad Mostafa Monowar S. M. Nuruzzaman Nobel Maharin Afroj Md Abdul Hamid Md Zia Uddin Md Mohsin Kabir M. F. Mridha |
| author_facet | Muhammad Mostafa Monowar S. M. Nuruzzaman Nobel Maharin Afroj Md Abdul Hamid Md Zia Uddin Md Mohsin Kabir M. F. Mridha |
| author_sort | Muhammad Mostafa Monowar |
| collection | DOAJ |
| description | Sleep disorder detection has greatly improved with the integration of machine learning, offering enhanced accuracy and effectiveness. However, the labor-intensive nature of diagnosis still presents challenges. To address these, we propose a novel coordination model aimed at improving detection accuracy and reliability through a multi-model ensemble approach. The proposed method employs a multi-layered ensemble model, starting with the careful selection of N models to capture essential features. Techniques such as thresholding, predictive scoring, and the conversion of Softmax labels into multidimensional feature vectors improve interpretability. Ensemble methods like voting and stacking are used to ensure collaborative decision-making across models. Both the original dataset and one modified using the Synthetic Minority Oversampling Technique (SMOTE) were evaluated to address data imbalance issues. The ensemble model demonstrated superior performance, achieving 96.88% accuracy on the SMOTE-implemented dataset and 95.75% accuracy on the original dataset. Moreover, an eight-fold cross-validation yielded an impressive 99.5% accuracy, indicating the reliability of the model in handling unbalanced data and ensuring precise detection of sleep disorders. Compared to individual models, the proposed ensemble method significantly outperformed traditional models. The combination of models not only enhanced accuracy but also improved the system's ability to handle unbalanced data, a common limitation in traditional methods. This study marks a significant advancement in sleep disorder detection through the integration of innovative ensemble techniques. The proposed approach, combining multiple models and advanced interpretability methods, promises improved patient outcomes and greater diagnostic accuracy, paving the way for future applications in medical diagnostics. |
| format | Article |
| id | doaj-art-bd46ae872b62445cbc0564c86747f4bf |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-bd46ae872b62445cbc0564c86747f4bf2025-01-28T11:57:45ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01710.3389/frai.2024.15067701506770Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniquesMuhammad Mostafa Monowar0S. M. Nuruzzaman Nobel1Maharin Afroj2Md Abdul Hamid3Md Zia Uddin4Md Mohsin Kabir5M. F. Mridha6Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, BangladeshDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, BangladeshFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaSustainable Communication Technologies, SINTEF Digital, Oslo, NorwaySuperior, Polytechnic School, University of Girona, Girona, SpainDepartment of Computer Science and Engineering, American International University, Dhaka, BangladeshSleep disorder detection has greatly improved with the integration of machine learning, offering enhanced accuracy and effectiveness. However, the labor-intensive nature of diagnosis still presents challenges. To address these, we propose a novel coordination model aimed at improving detection accuracy and reliability through a multi-model ensemble approach. The proposed method employs a multi-layered ensemble model, starting with the careful selection of N models to capture essential features. Techniques such as thresholding, predictive scoring, and the conversion of Softmax labels into multidimensional feature vectors improve interpretability. Ensemble methods like voting and stacking are used to ensure collaborative decision-making across models. Both the original dataset and one modified using the Synthetic Minority Oversampling Technique (SMOTE) were evaluated to address data imbalance issues. The ensemble model demonstrated superior performance, achieving 96.88% accuracy on the SMOTE-implemented dataset and 95.75% accuracy on the original dataset. Moreover, an eight-fold cross-validation yielded an impressive 99.5% accuracy, indicating the reliability of the model in handling unbalanced data and ensuring precise detection of sleep disorders. Compared to individual models, the proposed ensemble method significantly outperformed traditional models. The combination of models not only enhanced accuracy but also improved the system's ability to handle unbalanced data, a common limitation in traditional methods. This study marks a significant advancement in sleep disorder detection through the integration of innovative ensemble techniques. The proposed approach, combining multiple models and advanced interpretability methods, promises improved patient outcomes and greater diagnostic accuracy, paving the way for future applications in medical diagnostics.https://www.frontiersin.org/articles/10.3389/frai.2024.1506770/fullmachine learningsleep disorderensemble approachexplainable AIhealthcarediagnosis |
| spellingShingle | Muhammad Mostafa Monowar S. M. Nuruzzaman Nobel Maharin Afroj Md Abdul Hamid Md Zia Uddin Md Mohsin Kabir M. F. Mridha Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques Frontiers in Artificial Intelligence machine learning sleep disorder ensemble approach explainable AI healthcare diagnosis |
| title | Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques |
| title_full | Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques |
| title_fullStr | Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques |
| title_full_unstemmed | Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques |
| title_short | Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques |
| title_sort | advanced sleep disorder detection using multi layered ensemble learning and advanced data balancing techniques |
| topic | machine learning sleep disorder ensemble approach explainable AI healthcare diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1506770/full |
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