Department of Computer Engineering, Hongik University, Seoul, 04066, Republic of Korea.
Institute for Business Research and Education, Korea University, Seoul, 02841, Republic of Korea.
Sci Rep. 2023 Apr 19;13(1):6379. doi: 10.1038/s41598-023-33170-7.
As the incidence of obstructive sleep apnea syndrome (OSAS) increases worldwide, the need for a new screening method that can compensate for the shortcomings of the traditional diagnostic method, polysomnography (PSG), is emerging. In this study, data from 4014 patients were used, and both supervised and unsupervised learning methods were used. Clustering was conducted with hierarchical agglomerative clustering, K-means, bisecting K-means algorithm, Gaussian mixture model, and feature engineering was carried out using both medically researched methods and machine learning techniques. For classification, we used gradient boost-based models such as XGBoost, LightGBM, CatBoost, and Random Forest to predict the severity of OSAS. The developed model showed high performance with 88%, 88%, and 91% of classification accuracy for three thresholds for the severity of OSAS: Apnea-Hypopnea Index (AHI) [Formula: see text] 5, AHI [Formula: see text] 15, and AHI [Formula: see text] 30, respectively. The results of this study demonstrate significant evidence of sufficient potential to utilize machine learning in predicting OSAS severity.
随着阻塞性睡眠呼吸暂停综合征（OSAS）在全球范围内的发病率不断上升，需要一种新的筛查方法来弥补传统诊断方法（多导睡眠图 PSG）的不足。本研究使用了 4014 名患者的数据，并使用了监督和无监督学习方法。使用层次聚类、K 均值、二分 K 均值算法、高斯混合模型进行聚类，使用医学研究方法和机器学习技术进行特征工程。对于分类，我们使用基于梯度提升的模型，如 XGBoost、LightGBM、CatBoost 和随机森林，来预测 OSAS 的严重程度。所开发的模型在三个 OSAS 严重程度的阈值（呼吸暂停-低通气指数（AHI）[公式：见文本]5、AHI [公式：见文本]15 和 AHI [公式：见文本]30）下的分类准确率分别达到 88%、88%和 91%，显示出了较高的性能。本研究的结果证明了在预测 OSAS 严重程度方面充分利用机器学习的潜力。
