ArticleDiagnostics (Basel, Switzerland)2023
Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Exploring Biomarkers and Regulatory Mechanisms Associated with Lytic Cell Death in Allergic Rhinitis Based on Transcriptome Analysis.Biomedicines · 2026Article
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Multi-omics and machine learning reveal LYZ and ISG15 as diagnostic and therapeutic targets in autoimmune-mediated chronic kidney disease.Frontiers in immunology · 2026Article
- Review
- Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.Scientific reports · 2025Article
- RFID-embedded mattress for sleep disorder detection for athletes in sports psychology.Scientific reports · 2025Article
- Micro-computed tomographic evaluation of root and canal anatomy of maxillary first premolars in Iraqi sub-population.Scientific reports · 2025Article
- Identification of MAD2L1 as a novel biomarker for hepatoblastoma through bioinformatics and machine learning approaches.Frontiers in oncology · 2025Article
- Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experiments.Frontiers in immunology · 2025Article
- A multimodal multi-agent LLM framework for identifying key drivers of sleep disorders.Frontiers in neurologyArticle
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Authors and funding
6 authors.
Funding
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 was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were 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. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged 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.
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Registered trials
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