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Related Experiment Video

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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An Interpretable Machine Learning Framework for Analyzing the Interaction Between Cardiorespiratory Diseases and

Vito Telesca1, Maríca Rondinone1

  • 1Department of Engineering, University of Basilicata, 85100 Potenza, Italy.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models predict cardiorespiratory disease emergency room admissions based on environmental factors. High carbon monoxide, humidity, low pressure, and mild temperatures increase hospital admissions, aiding public health planning.

Keywords:
SHAP and LIME analysisair pollutionbenchmarking strategycardiorespiratory diseasesinterpretable machine learning

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Area of Science:

  • Environmental Health
  • Epidemiology
  • Machine Learning

Background:

  • Cardiorespiratory diseases (CRDs) pose a significant public health burden.
  • Environmental factors are increasingly recognized as contributors to CRD exacerbations.
  • Effective tools are needed to link environmental data with health outcomes for proactive management.

Purpose of the Study:

  • To develop and validate an interpretable machine learning (ML) framework.
  • To analyze the relationship between environmental factors and daily emergency room (ER) admissions for CRDs.
  • To identify key environmental variables and critical exposure thresholds influencing CRD admissions.

Main Methods:

  • Utilized eleven years (2013-2023) of health and environmental data (meteorological, air quality).
  • Compared four ML models, employing 10-fold cross-validation for reliability.
  • Applied SHAP for global model interpretability and LIME for local analysis and threshold identification.

Main Results:

  • XGBoost demonstrated superior predictive performance (R² = 0.901, MAE = 0.047).
  • Identified influential factors: high carbon monoxide (CO), relative humidity (RH), low atmospheric pressure (P_atm), and mild average temperature (Tavg).
  • Determined critical thresholds for increased CRD admission risk: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, RH > 70.33%.

Conclusions:

  • Interpretable ML models effectively link environmental conditions to CRD ER admissions.
  • Findings support enhanced public health surveillance and healthcare resource allocation.
  • The framework offers a valuable tool for environmental monitoring and preventative healthcare strategies.