Version 1
: Received: 11 December 2023 / Approved: 12 December 2023 / Online: 13 December 2023 (09:24:53 CET)
Version 2
: Received: 13 December 2023 / Approved: 13 December 2023 / Online: 14 December 2023 (03:08:25 CET)
Alazaidah, R.; Samara, G.; Aljaidi, M.; Haj Qasem, M.; Alsarhan, A.; Alshammari, M. Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models. Diagnostics 2023, 14, 27, doi:10.3390/diagnostics14010027.
Alazaidah, R.; Samara, G.; Aljaidi, M.; Haj Qasem, M.; Alsarhan, A.; Alshammari, M. Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models. Diagnostics 2023, 14, 27, doi:10.3390/diagnostics14010027.
Alazaidah, R.; Samara, G.; Aljaidi, M.; Haj Qasem, M.; Alsarhan, A.; Alshammari, M. Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models. Diagnostics 2023, 14, 27, doi:10.3390/diagnostics14010027.
Alazaidah, R.; Samara, G.; Aljaidi, M.; Haj Qasem, M.; Alsarhan, A.; Alshammari, M. Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models. Diagnostics 2023, 14, 27, doi:10.3390/diagnostics14010027.
Abstract
Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research is to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aims to meet three main objectives. These objectives are to identify the best regression model, the best classification model, and the best learning strategy that highly suits sleep disorder datasets. Considering two related datasets and several evaluation metrics that are related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty-three regression models. Also, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belong to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Ghassan Samara
Commenter's Conflict of Interests: Author