Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample.

Journal: Sleep
Published Date: Jul 13, 2020

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.

Authors

  • Wen-Chi Huang
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Pei-Lin Lee
    Center of Sleep Disorder, National Taiwan University Hospital, Taipei, Taiwan.
  • Yu-Ting Liu
  • Ambrose A Chiang
    Division of Pulmonary, Critical Care and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH.
  • Feipei Lai
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Room 410, Barry Lam Hall, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China.

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