Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample
Open Access
- 1 July 2020
- journal article
- research article
- Published by Oxford University Press (OUP) in Sleep
- Vol. 43 (7)
- https://doi.org/10.1093/sleep/zsz295
Abstract
Study Objectives: Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice. Methods: The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, >= 5/h, >= 15/h, and >= 30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (= 65 years old [y/o]). Results: Two features were selected to predict AHI cutoff >= 5/h with six features selected for >= 15/h, and six features selected for >= 30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male = 65 y/o. Conclusion: Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.Funding Information
- National Scientific Council (NSC102-2314-B002-099)
- Ministry of Science and Technology (MOST 103-2314-B-002-139-MY3)
- National Taiwan University (NTU-ERP-104R8951-1, 105R8951-1, 106R880301)
- National Taiwan University Hospital (NTUH 107-19, 108-S4331, NTU-107L900502, 108L900502)
- Ministry of Education
- NTU-NTUH-MediaTek Innovative Medical Electronics Research Center
This publication has 38 references indexed in Scilit:
- A survey on Data Mining approaches for HealthcareInternational Journal of Bio-Science and Bio-Technology, 2013
- Prediction of obstructive sleep apnea syndrome in a large Greek populationSleep and Breathing, 2010
- Cross-cultural comparison of the sleep-disordered breathing prevalence among Americans and JapaneseEuropean Respiratory Journal, 2010
- Obstructive sleep apnea has little impact on quality of life in the elderlySleep Medicine, 2009
- Gender differences in obstructive sleep apnea and treatment implicationsSleep Medicine Reviews, 2008
- Sleep Apnea and Cardiovascular Disease: An American Heart Association/American College of Cardiology Foundation Scientific Statement From the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing In Collaboration With the National Heart, Lung, and Blood Institute National Center on Sleep Disorders Research (National Institutes of Health)Journal of the American College of Cardiology, 2008
- Adult Obstructive Sleep Apnea: Pathophysiology and DiagnosisSocial psychiatry. Sozialpsychiatrie. Psychiatrie sociale, 2007
- Sleep apnea in the elderly: A specific entity?Sleep Medicine Reviews, 2007
- Prediction of obstructive sleep apnea in patients presenting to a tertiary care centerSleep and Breathing, 2006
- Predictive Value of Pulmonary Function Parameters for Sleep Apnea SyndromeAmerican Journal of Respiratory and Critical Care Medicine, 2000