Postoperative Apnea-Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning.

ABSTRACT:

Objective

To investigate machine learning-based regression models to predict the postoperative apnea-hypopnea index (AHI) for evaluating the outcome of velopharyngeal surgery in adult obstructive sleep apnea (OSA) subjects.

Study design

A single-center, retrospective, cohort study.

Setting

Sleep medical center.

Methods

All subjects with OSA who underwent velopharyngeal surgery followed for 3 to 6 months were enrolled in this study. Demographic, polysomnographic, and anatomical variables were analyzed. Compared with traditional stepwise linear regression (LR) algorithm, machine learning algorithms including artificial neural network (ANN), support vector regression, K-nearest neighbor, random forest, and extreme gradient boosting were utilized to establish the r

SUBMITTER: You J 

PROVIDER: S-EPMC11705500 | biostudies-literature | 2025 Jan-Mar

REPOSITORIES: biostudies-literature