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. 2024 Apr 24:16:413-428.
doi: 10.2147/NSS.S453794. eCollection 2024.

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

Jiewei Huang  1   2 , Jiajing Zhuang  2   3 , Huaxian Zheng  4 , Ling Yao  5   6 , Qingquan Chen  7   8 , Jiaqi Wang  1   2 , Chunmei Fan  1
Affiliations

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

Jiewei Huang et al. Nat Sci Sleep. .

Abstract

Objective: 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; machine learning; obstructive sleep apnea; prediction model; triglyceride-glucose index.

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Conflict of interest statement

The authors declare no conflicts of interest in this work.

Figures

None
Graphical abstract
Figure 1
Figure 1
The flowchart of preprocessing, dataset splitting, model selecting and final model training.
Figure 2
Figure 2
The OR value forest maps of the univariate and multivariate analysis. (A) The forest maps of OR values; (B) The forest maps of adjusted OR values.
Figure 3
Figure 3
The ROC curves of each variable. (A) The ROC curves of demographic variables. (B) The ROC curves of biochemical indicators.
Figure 4
Figure 4
The results of Boruta predictor importance analysis. In the figure, Tentative is yellow, Rejected is red, Accepted is green, and Shadow is blue.
Figure 5
Figure 5
Results of four prediction models. (A) The ROC curves of four prediction models in the training set; (B) The ROC curves of four prediction models in the validation set; (C) The forest plots of AUCs in each model; (D) The calibration curves of four prediction models.
Figure 6
Figure 6
Results of the LR prediction model. (A) The ROC curve of the LR model in the training set; (B) The ROC curve of the LR model in the validation set; (C) The ROC curve of the LR model in the test set; (D) The learning curve of the LR model; (E) The calibration curve of the LR model; (F) The clinical decision curve of the LR model.
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