Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea.
Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea.
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DOI：
10.4103/jrms.jrms_357_17
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Pavah BK
中科院分区：
文献类型：
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作者：
Manoochehri Z;Salari N;Rezaei M;Khazaie H;Manoochehri S;Pavah BK
阻塞性睡眠呼吸暂停（OSA）的诊断是医学上的一个重要课题。本研究旨在比较两种数据挖掘技术，支持向量机（SVM）和逻辑回归（LR），在诊断阻塞性睡眠呼吸暂停综合征的性能。最佳拟合模型被用作多导睡眠图（PSG）的替代品，PSG是诊断这种疾病的金标准。本研究共招募了250例有睡眠问题投诉的患者，这些患者的疾病已由PSG诊断，并在2012年至2015年期间转诊至克尔曼沙阿法拉比医院睡眠障碍研究中心。为了拟合最佳LR模型，首先用所有变量拟合模型，然后使用赤池信息准则（AIC）与由显着变量制成的模型进行比较。采用遗传算法优化参数的径向基函数（RBF）核和支持向量机（SVM）模型对阻塞性睡眠呼吸暂停进行诊断。基于AIC，从本研究中获得的最佳LR模型是与所有变量拟合的模型。将最终的LR模型与SVM模型的性能进行比较，分别揭示准确度0.797与0.729，灵敏度0.714与0.777，特异性0.847与0.702。这两种模式被认为有一个适当的性能。然而，考虑到准确性作为比较模型在该领域中的性能的重要标准，可以认为SVM在诊断患者中的OSA时可以具有比LR更好的效率。
Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. The best-fit model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease. A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To fit the best LR model, a model was first fitted with all variables and then compared with a model made from the significant variables using Akaike's information criterion (AIC). The SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA. Based on AIC, the best LR model obtained from this study was a model fitted with all variables. The performance of final LR model was compared with SVM model, revealing the accuracy 0.797 versus 0.729, sensitivity 0.714 versus 0.777, and specificity 0.847 vs. 0.702, respectively. Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efficiency than LR in diagnosing OSA in patients.