Optimized Prescreen Survey Tool for Predicting Sleep Apnea Based on Deep Neural Network: Pilot Study

Abstract

Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current study proposes reliable models that are based on machine learning techniques to predict the severity of OSA. A total of 66 participants consisted of 45 males and 21 females (average age = 52.4 years old; standard deviation ± 14.6). Participants were asked to fill out the questionnaire items. If the value of the Respiratory Disturbance Index (RDI) was more than 30, the participant was diagnosed with severe OSA. Several different modeling techniques were applied, including deep neural networks with a scaled principal component analysis (DNN-PCA), random forest (RF), Adaptive Boosting Classifier (ABC), Decision Tree Classifier (DTC), K-nearest neighbors classifier (KNC), and support vector machine classifier (SVMC). Among the participants, 27 participants were diagnosed with severe OSA (RDI > 30). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the developed models. As a result, the AUROC values of DNN-PCA, RF, ABC, DTC, KNC, and SVMC models were 0.95, 0.62, 0.53, 0.53, 0.51, and 0.78, respectively. The highest AUROC value was found in the DNN-PCA model with a sensitivity of 0.95, a specificity of 0.75, a positive predictivity of 0.95, an F1 score of 0.95, and an accuracy of 0.95. The DNN-PCA model outperforms the existing screening questionnaires, scores, and other models.