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Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and

Maciej Rosoł1, Jakub S Gąsior2, Kacper Korzeniewski1

  • 1Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland.

Journal of Clinical Medicine
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning accurately classifies pediatric individuals using cardiorespiratory data. Cardiorespiratory coupling features significantly enhance classification accuracy for health status assessment.

Keywords:
XAIcardiorespiratory couplingcardiorespiratory parameterscausalityhealth statusmachine learning

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Area of Science:

  • Cardiology
  • Computational Biology
  • Pediatrics

Background:

  • Pediatric cardiorespiratory health assessment is crucial.
  • Machine learning (ML) offers potential for objective classification.
  • Cardiorespiratory coupling (CRC) features may improve diagnostic accuracy.

Purpose of the Study:

  • Evaluate ML accuracy in classifying pediatric groups (patients, healthy, athletes).
  • Assess the impact of CRC features on classification performance.
  • Identify an optimal cardiorespiratory feature set for specialized applications.

Main Methods:

  • Utilized six datasets with varying cardiorespiratory parameters.
  • Applied multiple ML algorithms for subject classification.
  • Employed explainable artificial intelligence (XAI) for feature importance analysis.

Main Results:

  • Achieved over 89% accuracy using demographic, cardiac, respiratory, and CRC features.
  • Second best accuracy (85%) obtained with influential features excluding demographics.
  • CRC features significantly boosted classification accuracy, confirmed by XAI.

Conclusions:

  • ML and comprehensive cardiorespiratory features effectively classify pediatric health status.
  • ML and XAI show promise for advancing cardiorespiratory signal analysis.
  • Optimal feature set identified for potential use in training/rehabilitation monitoring.