Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

Authors:
Jorge L. M. Amaral;Agnaldo J. Lopes;José M. Jansen;Alvaro C. D. Faria;Pedro L. Melo
Affiliations:
Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil;Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil;Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil;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 J ...;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 J ...
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n=25; healthy, n=25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se87%, Sp94%, and AUC0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.