## 2 Related Research
### Application of Machine Learning  
Machine learning is widely employed in clinical analysis across the medical field, utilizing probabilistic estimation to elucidate the relationships between a dependent variable and one or more independent variables [11]. An illustrative example was provided by the 2022 study conducted by [PERSON], [PERSON], et al. that juxtaposed the diagnostic efficacy of sleep apnea using machine learning tools against that of the STOP-BANG questionnaire [12]. This research highlighted that machine learning predictive models are capable of facilitating the comprehensive identification of sleep apnea cases, including those where symptoms may not be reported or are unrecognized by patients [12]. Consequently, machine learning predictive tools are demonstrated to be effective in predicting sleep apnea in scenarios where patient survey errors or unacknowledged symptoms might otherwise impede diagnosis. In 2023, [PERSON], [PERSON], et al. introduced a machine learning model designed to provide accurate predictions and identify risk factors associated with the onset of sleep apnea [13]. Utilizing electronic health records from the Swedish National Study on Aging and Care (SNAC), this model integrated a comprehensive set of 75 features across 10,765 samples. The model's algorithm implemented a cross-validation approach, featuring an XGBoost module for assessing important features in the feature space and a BiLSTM (Bidirectional Long Short-Term Memory) module for classifying the probability of sleep apnea, achieving an accuracy of 97% with only the six most critical features from the dataset [13]. This underscores the model's utility in identifying risk factors and improving the diagnosis of sleep apnea. Furthermore, in 2023, [PERSON] and his research team developed a machine learning model based on clinical characteristics and questionnaires to predict OSA risk and analyze risk factors. Using data from 1656 patients who underwent multiple polysomnographies (PSGs) between 2018 and 2021, the study included 23 variables and added 15 variables after univariate analysis. To evaluate predictive performance, six classification models were employed: Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Bagging, and Multilayer Perceptron (MLP), with the GBM model showing the best performance [14]. This underscores the importance of integrating questionnaire-based approaches with machine learning models. Concurrently, in the same year, [PERSON], H. proposed a novel screening method aimed at addressing the limitations inherent in polysomnographies (PSGs). The methodology leveraged data from 4014 patients, applying both supervised and unsupervised learning techniques for data clustering, utilizing hierarchical agglomerative clustering, K-means, bisecting K-means algorithms, and Gaussian mixture models. The study embarked on feature engineering, harnessing both medically established methodologies and machine learning techniques. Through the deployment of gradient boost-based models, including XGBoost, LightGBM, CatBoost, and Random Forest for classification tasks, it achieved notable success in predicting the severity of obstructive sleep apnea (OSA). The classification accuracies were reported as 88%, 88%, and 91% for Apnea-Hypopnea Index (AHI) thresholds of 5, 15, and 30, respectively [15]. This demonstrates the potential of machine learning in predicting the severity of obstructive sleep apnea. Lastly, [PERSON], [PERSON], et al., in their 2023 work, summarized existing research on the role of machine learning in diagnosing, classifying, and treating sleep-related breathing disorders, reviewing studies up to January 2022 from Medline, EMBASE, and the Cochrane database [16].