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

  • Systems-Level Quality Improvement
  • Published:
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Abstract

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.

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Conflict of interest

The authors have no conflict of interest.

Author information

Authors and Affiliations

  1. Sleep Medicine Center and Department of Physical Medicine and Rehabilitation, Chung-Shan Medical University Hospital, Taichung, Taiwan, Republic of China

    Hua Ting

  2. Institute of Medicine, Chung-Shan Medical University, Taichung, Taiwan, Republic of China

    Hua Ting

  3. Department of Sport Management, National Taiwan University of Physical Education and Sport, Taichung, Taiwan, Republic of China

    Yi-Ting Mai

  4. School of Medical Informatics, Chung-Shan Medical University, Taichung, Taiwan, Republic of China

    Hsueh-Chen Hsu & Ming-Hseng Tseng

  5. School of Medical Sociology and Social Work, Chung Shan Medical University, Taichung, Taiwan, Republic of China

    Hui-Ching Wu

Authors
  1. Hua Ting
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  2. Yi-Ting Mai
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  3. Hsueh-Chen Hsu
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  4. Hui-Ching Wu
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  5. Ming-Hseng Tseng
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Corresponding author

Correspondence to Ming-Hseng Tseng.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Hua Ting and Hui-Ching Wu contributed equally to this study and share first authorship.

Appendix

Appendix

Table 9 Comparison of prediction OSA models
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Ting, H., Mai, YT., Hsu, HC. et al. Decision Tree Based Diagnostic System for Moderate to Severe Obstructive Sleep Apnea. J Med Syst 38, 94 (2014). https://doi.org/10.1007/s10916-014-0094-1

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  • DOI: https://doi.org/10.1007/s10916-014-0094-1

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