Work

Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm

Year: 2024

Type: article

Abstract: PURPOSE: More than 80% of patients with moderate to severe obstructive sleep apnea (OSA) are still not diagnosed timely. The prediction model based on random forest (RF) algorithm was established by using heart rate variability (HRV), clinical and demographic features so as to screen for the patients with high risk of moderate and severe obstructive sleep apnea. METHODS: The sleep monitoring data of 798 patients were randomly divided into training set (n = 558) and test set (n = 240) in 7:3 proportion. Grid search was applied to determine the best parameters of the RF model. 10-fold cross validation was utilized to evaluate the predictive performance of the RF model, which was then compared to the performance of the Logistic regression model. RESULTS: . The sensitivity, specificity, accuracy, F1 score and the area under receiver operating characteristic curve for RF model and Logistic regression model were 94.68% vs. 73.94%; 73.08% vs. 86.54%; 90.00% vs. 76.67%; 0.94 vs. 0.83 and 0.83 vs. 0.80 respectively. CONCLUSIONS: The RF prediction model can effectively distinguish patients with moderate to severe OSA, which is expected to carry out in a large-scale population in order to screening for high-risk patients, and helps to evaluate the effect of OSA treatment continuously.

Source: Sleep And Breathing

DOI: 10.1007/s11325-024-03151-9

Authors: Chenxu Zhang, Liangcai Yu, Lin Li, Ping Zeng, Xiaoqing Zhang

Institution: Sichuan University

Language: English

Topic: Obstructive Sleep Apnea Research

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,590