Predicting obstructive sleep apnea severity without polysomnography: a machine learning approach based on clinical and demographic features

Aims: Obstructive sleep apnea (OSA) is a prevalent disorder characterized by recurrent upper airway collapse during sleep, causing intermittent hypoxia and sleep fragmentation. Diagnosis depends on polysomnography (PSG), which is resource intensive and not always available. This study aimed to develop and validate a machine learning (ML) model to predict clinically significant OSA (apnea-hypopnea index [AHI]≥15) using only demographic, behavioral, symptomatic, and comorbidity data, without PSG-derived parameters. Methods: A retrospective dataset of 1281 adults evaluated for suspected OSA was analyzed. Variables included age, sex, body-mass index (BMI), neck circumference, Epworth Sleepiness Scale (ESS), smoking status, and comorbidities such as hypertension and diabetes. Four algorithms Random Forest (RF), Gradient Boosting, Logistic Regression, and Support Vector Machine were trained and tested using an 80/20 split and five-fold cross-validation. Model performance was assessed by accuracy, recall, precision, F1-score, and ROC-AUC. Results: Among 1281 participants, 42.6% had AHI≥15. The RF model achieved the best performance with 78.6% accuracy, 0.95 recall, 0.81 precision, 0.88 F1-score, and 0.86 ROC-AUC. ESS and BMI were the strongest predictors, followed by age, neck circumference, smoking, hypertension, and diabetes. The model effectively identified moderate-to-severe OSA cases, offering a practical triage tool for prioritizing PSG in resource-limited settings. Conclusion: The ML model accurately predicted clinically significant OSA using only accessible clinical variables. Its high sensitivity and interpretability support potential integration into clinical workflows for efficient risk stratification and PSG allocation. Prospective multicenter validation is warranted.