Work

Predicting Polysomnography‑derived Sleep Efficiency and Total Sleep Time Using Clinical Questionnaires: A Machine Learning Approach

Year: 2026

Type: article

Abstract: Objective: We developed and evaluated machine learning models to predict polysomnography (PSG)-derived sleep efficiency and total sleep time using clinical questionnaire-based assessments, with the aim of developing accessible non-invasive screening tools that can estimate objective sleep parameters in the absence of PSG. Methods: Predictive models were developed using demographic data and nine validated questionnaires from 1,135 participants. Three gradient boosting algorithms-Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGBoost)-were compared for binary classification of sleep efficiency (≥ 85% vs. < 85%) and four-class prediction of total sleep time (< 5, 5-7, 7-9, and ≥ 9 hours). Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC) metrics. Results: CatBoost consistently outperformed LGBM and XGBoost. For sleep efficiency classification classification, CatBoost achieved the highest performance (AUC = 0.7342; PRC = 0.6957). In total sleep time prediction, CatBoost demonstrated superior accuracy (weighted AUC = 0.6772; weighted PRC = 0.5384; micro-AUC = 0.8195). Inclusion of formal sleep disorder diagnoses provided negligible improvements. Conclusion: Validated self-report instruments effectively captured clinically meaningful determinants of sleep efficiency and total sleep time. Machine learning models, particularly CatBoost, demonstrated robust performance using questionnaire-derived features alone, highlighting the central roles of age, insomnia severity, and daytime sleepiness. These findings support the feasibility and clinical utility of accessible, questionnaire-driven machine learning approaches for large-scale screening, early risk stratification, and decision support in sleep medicine.

Source: Clinical Psychopharmacology and Neuroscience

DOI: 10.9758/cpn.25.1354