Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults

BMC Medical Informatics and Decision Making

Research

Zine Cao¹  Haiqin Liu¹  Liang Xing¹  Yewen Shi¹  Yushan Xie¹  Lina Ma¹  Xiaoxin Niu¹  Xi Chen¹  Yonglong Su¹  Xiaoyong Ren¹  Yuqi Yuan¹  Yitong Zhang¹  Wenle Li²  Xinhong Hei³  Shinan Wu⁴ 

[1]Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, NO. 157 Xi Wu Road, Xi’an, Shaan’xi Province, China

[2]Molecular Imaging and Translational Medicine Research Center, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, Fujian Province, China

[3]School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaan’xi Province, China

[4]School of Medicine, Eye Institute of Xiamen University, Xiamen University, Xiamen, Fujian Province, China

关键词: Obstructive sleep apnea;    Prediction model;    Machine learning;    Risk factor;    Shapley additive explanations;    Gradient boosting machine;   

DOI  :  10.1186/s12911-023-02331-z

 received in 2023-06-07, accepted in 2023-10-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires.MethodsThis was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals.ResultsAmong the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient.ConclusionsWe established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment.Trial registrationRetrospectively registered.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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