Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.

BACKGROUND

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) has a high global prevalence and is prone to causing various serious complications. Our objective is to develop severity stratification of OSAHS by integrating multiple commonly available clinical features based on machine learning (ML).

MATERIALS AND

METHODS

This study collected data from 432 cases at Qujing Central Hospital in Yunnan Province, integrating 25 clinical feature variables. The cases were randomly split into training (70%) and validation (30%) sets. The importance of the 25 features was analyzed.

RESULTS

It showed that the HCY, TBIL, BMI, GGT, and Age made significant contributions to OSAHS severity. We established five machine learning models-Multilayer Perceptron (MLP), Random Forest, XGBoost, LightGBM, and Support Vector Machine (SVM)-by integrating 25 clinical features. Through cross-validation and continuous adjustment of model parameters, the optimal predictive model was determined. By calculating model accuracy and F1-score, XGBoost was identified as the best-performing model, achieving an area under the curve (AUC) of 0.63, an accuracy of 75% and an F1-score of 65.60.

CONCLUSION

In this study, we established a predictive model for the severity stratification of OSAHS based on machine learning algorithms. The XGBoost model demonstrated superior predictive performance.