Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, mach
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ORIGINAL ARTICLE
Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers Jorge L. M. Amaral 1 & Alexandre G. Sancho 2 & Alvaro C. D. Faria 2 & Agnaldo J. Lopes 3 & Pedro L. Melo 2 Received: 24 January 2020 / Accepted: 26 July 2020 # International Federation for Medical and Biological Engineering 2020
Abstract To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.
Keywords Clinical decision support system . Forced oscillation technique . Diagnostic of respiratory diseases . Respiratory oscillometry . Differential diagnosis Abbreviations ADAB AdaBoost classifier with decision trees. It is a ML algorithm that employs an ensemble strategy called boosting. Each base estimator (decision tree) is designed to correctly classify the instances misclassified by previous base Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11517-020-02240-7) contains supplementary material, which is available to authorized users.
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* Pedro L. Melo [email protected] 1
Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
2
Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil
3
Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil
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estimators. The final output is a weighted combination of all base estimators (decision trees) The fuzzy set of the jth feature in the ith rule Area un
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