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Updated: Dec 26, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Published on: September 19, 2025

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Visually guided classification trees for analyzing chronic patients.

Cristina Soguero-Ruiz1, Inmaculada Mora-Jiménez2, Miguel A Mohedano-Munoz3

  • 1Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Fuenlabrada, Spain. cristina.soguero@urjc.es.

BMC Bioinformatics
|March 14, 2020
PubMed
Summary
This summary is machine-generated.

This study uses visually guided classification trees to analyze patient data, improving the understanding of chronic diseases like diabetes and hypertension. The method helps identify key factors for classifying patient health statuses, aiding clinical insights.

Keywords:
Chronic health statusClassification treesDiabetesDiagnosesDrugsHypertensionMultivariate visualization

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Area of Science:

  • Medical Informatics
  • Data Science in Healthcare
  • Clinical Decision Support

Background:

  • Chronic diseases, including diabetes mellitus (DM) and essential hypertension (EH), are increasingly prevalent due to rising life expectancy.
  • These conditions can lead to other serious health issues like kidney or obstructive pulmonary diseases.
  • Understanding the factors contributing to complex chronic diseases is crucial for developing effective diagnostic and analytical methods.

Purpose of the Study:

  • To develop and evaluate a visually guided methodology for constructing classification trees.
  • To identify discriminative decision features for classifying patients into different health statuses.
  • To provide clinicians with tools for discovering new clinical insights and understanding complex disease factors.

Main Methods:

  • Analysis of patient data from the University Hospital of Fuenlabrada, Spain.
  • Classification of patients into health statuses using clinical risk groups (CRGs) based on age, gender, diagnosis, and drug codes.
  • Construction of classification trees using statistical data visualizations to guide feature selection at each node.

Main Results:

  • Classification trees were created to distinguish between healthy, single chronic condition, and comorbid patients.
  • Performance was evaluated based on classification accuracy.
  • Patients with comorbidities were harder to classify than those with single conditions or no chronic diseases.
  • Antipsychotic use and chronic airway obstruction diagnosis were identified as relevant features for classifying patients with multiple chronic conditions, alongside DM and EH.

Conclusions:

  • A visually guided methodology for constructing classification trees was proposed.
  • This approach enables progressive feature selection by clinicians.
  • Exploratory data analysis visualizations can reveal novel and unexpected clinical information.