Jorge L M Amaral1, Agnaldo J Lopes2, Juliana Veiga3, Alvaro C D Faria3, Pedro L Melo4. 1. Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil. 2. Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil. 3. Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil. 4. Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil. Electronic address: plopes@uerj.br.
Abstract
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.
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 asthmapatients, and they can assist clinicians in airway obstruction identification.
Authors: Allan Danilo de Lima; Agnaldo J Lopes; Jorge Luis Machado do Amaral; Pedro Lopes de Melo Journal: BMC Med Inform Decis Mak Date: 2022-10-20 Impact factor: 3.298
Authors: Cirlene de Lima Marinho; Maria Christina Paixão Maioli; Jorge Luis Machado do Amaral; Agnaldo José Lopes; Pedro Lopes de Melo Journal: PLoS One Date: 2017-12-08 Impact factor: 3.240
Authors: Cirlene de Lima Marinho; Maria Christina Paixão Maioli; Jorge Luis Machado do Amaral; Agnaldo José Lopes; Pedro Lopes de Melo Journal: PLoS One Date: 2019-03-07 Impact factor: 3.240
Authors: Fábio Augusto D Alegria Tuza; Paula Morisco de Sá; Hermano A Castro; Agnaldo José Lopes; Pedro Lopes de Melo Journal: Biomed Eng Online Date: 2020-12-09 Impact factor: 2.819
Authors: Domingos S M Andrade; Luigi Maciel Ribeiro; Agnaldo J Lopes; Jorge L M Amaral; Pedro L Melo Journal: Biomed Eng Online Date: 2021-03-25 Impact factor: 2.819
Authors: Chin-Chuan Shih; Ssu-Han Chen; Gin-Den Chen; Chi-Chang Chang; Yu-Lin Shih Journal: Int J Environ Res Public Health Date: 2021-12-04 Impact factor: 3.390