A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea

• Published in: Medical & biological engineering & computing Vol. 58; no. 10; pp. 2517 - 2529
• Main Authors: Hajipour, Farahnaz, Jozani, Mohammad Jafari, Moussavi, Zahra
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020; Springer Nature B.V
• Subjects: Acoustics; Apnea; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification; Computer Applications; Data analysis; Daytime; Dimensional analysis; Human Physiology; Imaging; Learning algorithms; Machine learning; Medical diagnosis; Original Article; Radiology; Regression analysis; Screening; Sensitivity analysis; Sleep; Sleep and wakefulness; Sleep apnea; Sleep disorders; Wakefulness; Regularized logistic regression; Feature selection; Random forest; Obstructive sleep apnea; Classification; LASSO
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-020-02206-9

A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess the utility of two important machine-learning techniques to classify subjects with obstructive sleep apnea (OSA) using their daytime tracheal breathing sounds. We evaluate and compare the performance of the random forest (RF) and regularized logistic regression (LR) as feature selection tools and classification approaches for wakefulness OSA screening. Results show that the RF, which is a low-variance committee-based approach, outperforms the regularized LR in terms of blind-testing accuracy, specificity, and sensitivity with 3.5%, 2.4%, and 3.7% improvement, respectively. However, the regularized LR was found to be faster than the RF and resulted in a more parsimonious model. Consequently, both the RF and regularized LR feature reduction and classification approaches are qualified to be applied for the daytime OSA screening studies, depending on the nature of data and applications’ purposes. Graphical Abstract