Kim Young Jae, Jeon Ji Soo, Cho Seo-Eun, Kim Kwang Gi, Kang Seung-Gul
Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea.
Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea.
Diagnostics (Basel). 2021 Mar 30;11(4):612. doi: 10.3390/diagnostics11040612.
This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, = 213; no OSA, = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, = 149; no OSA, = 46) and test data (OSA, = 64; no OSA, = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models-logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)-and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.
本研究旨在调查机器学习在预测韩国疑似阻塞性睡眠呼吸暂停（OSA）个体中的适用性。从279名韩国人（OSA患者213例；非OSA患者66例）中收集了92个OSA临床变量，从中选择了七个主要临床指标。数据被随机分为训练数据（OSA患者149例；非OSA患者46例）和测试数据（OSA患者64例；非OSA患者20例）。使用这七个临床指标，利用四种机器学习模型——逻辑回归、支持向量机（SVM）、随机森林和极端梯度提升（XGB）——训练OSA预测模型，并使用测试数据对每个模型进行验证。在验证过程中，SVM显示出最佳的OSA预测结果，敏感性、特异性和曲线下面积（AUC）分别为80.33%、86.96%和0.87，而XGB的OSA预测性能最低，敏感性、特异性和AUC分别为78.69%、73.91%和0.80。机器学习算法使用韩国疑似OSA个体的数据显示出较高的OSA预测性能。因此，机器学习将有助于韩国人群OSA预测的临床应用。
