High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements
High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements
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DOI：
10.1016/j.cmpb.2017.03.023
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发表时间：
2017-06-01
影响因子：
6.1
通讯作者：
Melo, Pedro L.
中科院分区：
文献类型：
--
作者：
Amaral, Jorge L. M.;Lopes, Agnaldo J.;Melo, Pedro L.
背景和目的：哮喘的主要病理特征是发作性气道阻塞。这通常通过肺活量测定法和身体体积描记法来检测。然而，这些测试需要患者的高度合作和最大努力。文献一致认为，需要研究新技术来改善肺功能的无创检测。本研究的目的是开发自动分类器，以简化临床使用并提高强制振荡技术 (FOT) 在诊断哮喘患者气道阻塞时的准确性。 方法：数据包括从 75 名志愿者（39 名患有阻塞，36 名没有阻塞）获得的 FOT 参数。研究了不同的监督机器学习 (ML) 技术，包括 k 最近邻 (KNN)、随机森林 (RF)、带有决策树的 AdaBoost (ADAB) 和基于特征的相异空间分类器 (FDSC)。 结果：本研究的第一部分表明，最佳 FOT 参数是共振频率 (AUC= 0.81)，这表明准确度中等 (0.70-0.90)。在本研究的第二部分中，研究了所引用的机器学习技术的使用。所有分类器都提高了诊断准确性。值得注意的是，ADAB 和 KNN 非常接近实现高精度（AUC 分别为 0.88 和 0.89）。包括FOT参数叉积在内的实验表明，所有分类器都提高了诊断准确性，并且KNN能够达到更高的准确性范围（AUC=0.91）。结论：机器学习分类器可以帮助诊断哮喘患者气道阻塞，并可以辅助临床医生进行气道阻塞识别。 (C) 2017 Elsevier B.V. 保留所有权利。
Background and Objectives: The main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the forced oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma.Methods: The data consisted of FOT parameters obtained from 75 volunteers (39 with obstruction and 36 without). Different supervised machine learning (ML) techniques were investigated, including k-nearest neighbors (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC).Results: The first part of this study showed that the best FOT parameter was the resonance frequency (AUC= 0.81), which indicates moderate accuracy (0.70-0.90). In the second part of this study, the use of the cited ML techniques was investigated. All the classifiers improved the diagnostic accuracy. Notably, ADAB and KNN were very close to achieving high accuracy (AUC=0.88 and 0.89, respectively). Experiments including the cross products of the FOT parameters showed that all the classifiers improved the diagnosis accuracy and KNN was able to reach a higher accuracy range (AUC= 0.91).Conclusions: Machine learning classifiers can help in the diagnosis of airway obstruction in asthma patients, and they can assist clinicians in airway obstruction identification. (C) 2017 Elsevier B.V. All rights reserved.