Decision tree based diagnostic system for moderate to severe obstructive sleep apnea

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.