Predicting Surgical Outcomes in Obstructive Sleep Apnea Patients Using Machine Learning Approach

Obstructive Sleep Apnea (OSA) is a prevalent respiratory sleep disorder, with its pathogenesis potentially associated with airway obstruction influenced by physical factors such as BMI, neck circumference, age, and sex. Surgical intervention is often undertaken by some patients to mitigate the severity of this breathing disorder. However, not all OSA patients benefit uniformly from surgical intervention. To address this variability, we propose a method aimed at predicting the efficacy of surgery, focusing on identifying prognostic indicators that effectively predict the reduction in the apnea-hypopnea index (AHI) post-surgery. We investigated and compared two machine learning models, k-Nearest Neighbors (k-NN) and Logistic Regression (LR), to predict whether there would be a positive response in AHI following surgical intervention in OSA patients. Data were collected from 33 participants before (AHI range: 11.20-120.60) and after OSA surgery at National Cheng Kung Hospital. The dataset includes polysomnography (PSG) indices such as AHI, Apnea Index, and Hypopnea Index, and demographic variables including BMI, age, sex, and neck circumference. The results were validated through stratified 5-fold cross-validation. The k-NN model, with Manhattan distance and distance weighting, demonstrated an accuracy of 90%, sensitivity of 80%, specificity of 100%, and Sn + Sp - 1 of 0.80, outperforming the Logistic Regression model, which achieved an accuracy of 80%, sensitivity of 60%, specificity of 100%, and Sn + Sp - 1 of 0.60. Additionally, feature importance analysis from the Logistic Regression model identified HI, BMI, and AHI as the most significant predictors, providing insights into the key features influencing surgical outcomes. These findings suggest that the k-NN model is a robust tool for predicting surgical outcomes in OSA patients, with potential for clinical application in personalized treatment planning.