A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Abstract

Jiewei Huang,1,2 Jiajing Zhuang,2,3 Huaxian Zheng,4 Ling Yao,5,6 Qingquan Chen,7,8 Jiaqi Wang,1,2 Chunmei Fan1 1The Clinical Laboratory Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362018, People’s Republic of China; 2The Graduate School of Fujian Medical University, Fuzhou, Fujian Province, 350108, People’s Republic of China; 3Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, People’s Republic of China; 4The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, 350108, People’s Republic of China; 5Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350000, People’s Republic of China; 6Department of Nephrology, Rheumatology and Immunology, Fujian Children’s Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350014, People’s Republic of China; 7The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362018, People’s Republic of China; 8The School of Public Health, Fujian Medical University, Fuzhou, Fujian Province, 350108, People’s Republic of ChinaCorrespondence: Chunmei Fan, The Clinical Laboratory Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362018, People’s Republic of China, Tel [phone], Email [email protected]: Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening.Methods: The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were “Gaussian Naive Bayes (GNB)”, “Support Vector Machine (SVM)”, “K Neighbors Classifier (KNN)”, and “Logistic Regression (LR)”. Finally, the optimal algorithm was selected to construct the final prediction model.Results: Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively.Conclusion: We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG. Keywords: biochemical indicators, obstructive sleep apnea, machine learning, prediction model, triglyceride-glucose index

Keywords

• biochemical indicators
• obstructive sleep apnea
• machine learning
• prediction model；triglyceride-glucose index