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.

在大型和高维数据分析中，主要挑战在于通过表征特征因子与预测变量之间的关系来对目标变量进行分类和预测。这项研究旨在评估两种重要的机器学习技术的实用性，该技术利用白天的气管呼吸声对阻塞性睡眠呼吸暂停（OSA）的受试者进行分类。我们评估和比较随机森林（RF）和正则逻辑回归（LR）作为清醒OSA筛选的特征选择工具和分类方法的性能。结果表明，RF是一种基于低差异委员会的方法，在盲检准确性，特异性和敏感性方面，均优于常规LR，分别提高了3.5％，2.4％和3.7％。然而，发现正规化的LR比RF快，并且导致了更简化的模型。因此，根据数据的性质和应用目的，RF和常规LR特征缩减和分类方法都可以用于白天OSA筛选研究。