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A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.
Huang, Jiewei; Zhuang, Jiajing; Zheng, Huaxian; Yao, Ling; Chen, Qingquan; Wang, Jiaqi; Fan, Chunmei.
Afiliación
  • Huang J; The Clinical Laboratory Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362018, People's Republic of China.
  • Zhuang J; The Graduate School of Fujian Medical University, Fuzhou, Fujian Province, 350108, People's Republic of China.
  • Zheng H; The Graduate School of Fujian Medical University, Fuzhou, Fujian Province, 350108, People's Republic of China.
  • Yao L; Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, People's Republic of China.
  • Chen Q; The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, 350108, People's Republic of China.
  • Wang J; Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350000, People's Republic of China.
  • Fan C; Department 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.
Nat Sci Sleep ; 16: 413-428, 2024.
Article en En | MEDLINE | ID: mdl-38699466
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Sci Sleep Año: 2024 Tipo del documento: Article Pais de publicación: Nueva Zelanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Sci Sleep Año: 2024 Tipo del documento: Article Pais de publicación: Nueva Zelanda