ArticleNature and science of sleep2025
Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA.
Article in Nature and science of sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Artificial Intelligence Virtual Organoids (AIVOs).Bioactive materials · 2026Review
- Development and Validation of an Explainable Machine Learning Model for Identifying Hypertension Status in Patients With Obstructive Sleep Apnea.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
- Preoperative risk stratification for pathological upgrading in colorectal polyps using explainable machine learning: implications for screening optimization and resource allocation.Frontiers in public health · 2026Article
- Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA [Response to Letter].Nature and science of sleep · 2025Article
- Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA [Letter].Nature and science of sleep · 2025Article
- From Model to Bedside: What Kind of OSA Risk Prediction Tools Do We Need More of? [Letter].Nature and science of sleep · 2025Article
- From Model to Bedside: What Kind of OSA Risk Prediction Tools Do We Need More Of? [Response to Letter].Nature and science of sleep · 2025Article
- Explainable machine learning reveals multifactorial drivers of early intracranial hematoma progression in traumatic brain injury: development of a SHAP-guided SVM nomogram.Frontiers in neurologyArticle
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Authors and funding
7 authors.
Funding
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Abstract
Objective: The aim of this study was to establish a risk prediction model for sleep quality in patients with obstructive sleep apnea (OSA) based on machine learning algorithms with optimal predictive performance. Methods: A total of 400 OSA patients were included in this study. A LightGBM model was constructed and compared with other machine learning models, in terms of performance metrics such as the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The SHapley Additive exPlanation (SHAP) analysis was used to interpret the model and identify key predictors of sleep quality. Results: The LightGBM model demonstrated the best predictive performance, with an AUC of 0.910 in the validation set, outperforming support vector machine and random forest. SHAP analysis identified six key predictors of sleep quality: depressive symptoms, OSA duration, oxygen desaturation index (ODI), anxiety symptoms, exercise frequency, and coffee consumption. The model's calibration curve indicated a high degree of agreement between predicted and observed outcomes, and DCA confirmed its clinical utility. Conclusion: The LightGBM model is the best choice for predicting sleep quality in patients with OSA. Depressive symptoms and ODI were the most influential factors negatively associated with sleep quality. This study not only deepens understanding of the factors affecting sleep quality in OSA patients, but also provides a powerful predictive tool for clinical doctors. Future research can explore the potential of incorporating these predictive factors into comprehensive treatment strategies to improve patient prognosis and overall quality of life.
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