Machine Learning Models to Enhance the Berlin Questionnaire Detection of Obstructive Sleep Apnea in at-Risk Patients

Abstract

The Berlin questionnaire (BQ), with its ten questions, stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting individuals at a high risk of Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by the partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. Another ML model (ML-2) was trained using a simplified variant of the BQ, BQ-2, which comprises only two questions out of the total ten. A 10-fold cross validation scheme was employed. Ground truth was provided by the Apnea–Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. The model performance was determined by comparing ML-10 and ML-2 with the standard BQ in the Receiver Operating Characteristic (ROC) space and using metrics such as the Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Both ML-10 and ML-2 demonstrated superior performance in predicting the risk of OSA compared to the standard BQ and were also capable of classifying OSA with two different AHI thresholds (AHI ≥ 15, AHI ≥ 30) that are typically used in clinical practice. This study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine with minimal effort.