Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis.

Journal: Biomedical engineering online
PMID: 33766046

Abstract

INTRODUCTION: The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.

Authors

  • Domingos S M Andrade
    Electronic Engineering Post-Graduation Program, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Luigi Maciel Ribeiro
    Electronic Engineering Post-Graduation Program, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Agnaldo J Lopes
    Pulmonary Function Laboratory, Pedro Ernesto University Hospital, Brazil.
  • Jorge L M Amaral
    Department of Electronics and Telecommunications Engineering, Brazil.
  • Pedro L Melo
    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.

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