Journal of Medical Systems

, 38:94 | Cite as

Decision Tree Based Diagnostic System for Moderate to Severe Obstructive Sleep Apnea

  • Hua Ting
  • Yi-Ting Mai
  • Hsueh-Chen Hsu
  • Hui-Ching Wu
  • Ming-Hseng TsengEmail author
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


One of the major modern medical issues, obstructive sleep apnea (OSA), particularly at moderate to severe levels, may potentially cause cardiovascular morbidity and mortality. However, polysomnography (PSG), a gold standard tool in diagnosing OSA, is cumbersome, has limited availability, and is costly and time-consuming. Clinical prediction models thus are absolutely necessary in screening patients with OSA. Furthermore, the performance of the published prediction formulas is not satisfactory for Chinese populations. The aim of this study was to develop and validate a simple and accurate prediction system for the diagnosis of moderate to severe OSA by integrating an expert-based feature extraction technique with decision tree algorithms which have automatic feature selection capability in screening the moderate to severe OSA cases in Taiwan. Moreover, the backward stepwise multivariable logistic regression model and four other decision tree algorithms were also employed for comparison. The results showed that the proposed best prediction formula, with an overall accuracy reaching to 96.9 % in sensitivity = 98.2 % and specificity = 93.2 %, could present a good tool for screening moderate and severe Taiwanese OSA patients who require further PSG evaluation and medical intervention. Results also indicate that the proposed best prediction formula is simple, accurate, and reliable, and outperforms all the other prediction formulae considered in the present study. The proposed clinical prediction formula derived from three non-invasive features (Sex, Age, and AveSBP) may help prioritize patients for PSG studies as well as avoid a diagnosis of PSG in subjects who have a low probability of having the disease.


Obstructive sleep apnea Decision tree algorithms Feature extraction Logistic regression model 


Conflict of interest

The authors have no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Hua Ting
    • 1
    • 2
  • Yi-Ting Mai
    • 3
  • Hsueh-Chen Hsu
    • 4
  • Hui-Ching Wu
    • 5
  • Ming-Hseng Tseng
    • 4
    Email author
  1. 1.Sleep Medicine Center and Department of Physical Medicine and RehabilitationChung-Shan Medical University HospitalTaichungRepublic of China
  2. 2.Institute of MedicineChung-Shan Medical UniversityTaichungRepublic of China
  3. 3.Department of Sport ManagementNational Taiwan University of Physical Education and SportTaichungRepublic of China
  4. 4.School of Medical InformaticsChung-Shan Medical UniversityTaichungRepublic of China
  5. 5.School of Medical Sociology and Social WorkChung Shan Medical UniversityTaichungRepublic of China

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