Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches
Classifying sleep disorders, such as obstructive sleep apnea and insomnia, is crucial for improving human quality of life due to their significant impact on health. The traditional expert-based classification of sleep stages, particularly through visual inspection, is challenging and prone to errors...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856004/ |
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| author | Md. Atiqur Rahman Israt Jahan Maheen Islam Taskeed Jabid Md Sawkat Ali Mohammad Rifat Ahmmad Rashid Mohammad Manzurul Islam Md. Hasanul Ferdaus Md Mostofa Kamal Rasel Mahmuda Rawnak Jahan Shayla Sharmin Tanzina Afroz Rimi Atia Sanjida Talukder Md. Mafiul Hasan Matin M. Ameer Ali |
| author_facet | Md. Atiqur Rahman Israt Jahan Maheen Islam Taskeed Jabid Md Sawkat Ali Mohammad Rifat Ahmmad Rashid Mohammad Manzurul Islam Md. Hasanul Ferdaus Md Mostofa Kamal Rasel Mahmuda Rawnak Jahan Shayla Sharmin Tanzina Afroz Rimi Atia Sanjida Talukder Md. Mafiul Hasan Matin M. Ameer Ali |
| author_sort | Md. Atiqur Rahman |
| collection | DOAJ |
| description | Classifying sleep disorders, such as obstructive sleep apnea and insomnia, is crucial for improving human quality of life due to their significant impact on health. The traditional expert-based classification of sleep stages, particularly through visual inspection, is challenging and prone to errors. This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. This paper compares the MLAs for sleep disorder classification, specifically targeting None, Sleep Apnea, and Insomnia, using the Sleep Health and Lifestyle Dataset. We conducted two experiments. In the first one, we selected five key features from the feature spaces using the Gradient Boosting Regressor based on the Mean Decrease Impurity (MDI) technique. We chose two key features using the same methodology in the second experiment. We utilized 15 machine learning classifiers, and Gradient Boosting, Voting, Catboost, and Stacking Classifiers achieved an identical classification accuracy of 97.33%, with Precision, Recall, F1-score of 0.9733, and Specificity of 0.9569 in the original feature space. Among these, Gradient Boosting had the highest AUC of 0.9953 and was 3.36, 5.86, and 20.16 times faster than Voting, Catboost, and Stacking Classifiers, respectively. In the second experiment, the Decision Tree achieved the highest accuracy of 96% in the original and engineered feature spaces and was 149.33 times faster in the engineered feature space. Thus, this research proposes Gradient Boosting as the most effective method, outperforming all state-of-the-art techniques by achieving the highest accuracy, precision, recall, F1-score, and AUC, highlighting its superior classification performance and computational efficiency. |
| format | Article |
| id | doaj-art-343e234a2dc845cfabe5bff6c6128c5d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-343e234a2dc845cfabe5bff6c6128c5d2025-02-05T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113209892100410.1109/ACCESS.2025.353553510856004Improving Sleep Disorder Diagnosis Through Optimized Machine Learning ApproachesMd. Atiqur Rahman0https://orcid.org/0000-0003-3450-6416Israt Jahan1https://orcid.org/0009-0005-9406-6223Maheen Islam2https://orcid.org/0000-0002-8292-0059Taskeed Jabid3Md Sawkat Ali4Mohammad Rifat Ahmmad Rashid5Mohammad Manzurul Islam6https://orcid.org/0000-0002-3008-081XMd. Hasanul Ferdaus7Md Mostofa Kamal Rasel8https://orcid.org/0000-0003-3051-3838Mahmuda Rawnak Jahan9Shayla Sharmin10https://orcid.org/0009-0008-3407-7299Tanzina Afroz Rimi11https://orcid.org/0000-0001-5710-3148Atia Sanjida Talukder12https://orcid.org/0009-0006-1977-2598Md. Mafiul Hasan Matin13M. Ameer Ali14Department of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshTesserent, Melbourne, VIC, AustraliaClassifying sleep disorders, such as obstructive sleep apnea and insomnia, is crucial for improving human quality of life due to their significant impact on health. The traditional expert-based classification of sleep stages, particularly through visual inspection, is challenging and prone to errors. This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. This paper compares the MLAs for sleep disorder classification, specifically targeting None, Sleep Apnea, and Insomnia, using the Sleep Health and Lifestyle Dataset. We conducted two experiments. In the first one, we selected five key features from the feature spaces using the Gradient Boosting Regressor based on the Mean Decrease Impurity (MDI) technique. We chose two key features using the same methodology in the second experiment. We utilized 15 machine learning classifiers, and Gradient Boosting, Voting, Catboost, and Stacking Classifiers achieved an identical classification accuracy of 97.33%, with Precision, Recall, F1-score of 0.9733, and Specificity of 0.9569 in the original feature space. Among these, Gradient Boosting had the highest AUC of 0.9953 and was 3.36, 5.86, and 20.16 times faster than Voting, Catboost, and Stacking Classifiers, respectively. In the second experiment, the Decision Tree achieved the highest accuracy of 96% in the original and engineered feature spaces and was 149.33 times faster in the engineered feature space. Thus, this research proposes Gradient Boosting as the most effective method, outperforming all state-of-the-art techniques by achieving the highest accuracy, precision, recall, F1-score, and AUC, highlighting its superior classification performance and computational efficiency.https://ieeexplore.ieee.org/document/10856004/SMOTEENNANOVA testfeature engineeringclassificationsleep disordermachine learning classifiers |
| spellingShingle | Md. Atiqur Rahman Israt Jahan Maheen Islam Taskeed Jabid Md Sawkat Ali Mohammad Rifat Ahmmad Rashid Mohammad Manzurul Islam Md. Hasanul Ferdaus Md Mostofa Kamal Rasel Mahmuda Rawnak Jahan Shayla Sharmin Tanzina Afroz Rimi Atia Sanjida Talukder Md. Mafiul Hasan Matin M. Ameer Ali Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches IEEE Access SMOTEENN ANOVA test feature engineering classification sleep disorder machine learning classifiers |
| title | Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches |
| title_full | Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches |
| title_fullStr | Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches |
| title_full_unstemmed | Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches |
| title_short | Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches |
| title_sort | improving sleep disorder diagnosis through optimized machine learning approaches |
| topic | SMOTEENN ANOVA test feature engineering classification sleep disorder machine learning classifiers |
| url | https://ieeexplore.ieee.org/document/10856004/ |
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