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Related Experiment Video

Updated: Jan 9, 2026

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

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Published on: April 19, 2019

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Development of Machine Learning Models for Predicting Effectiveness and Adherence in Cardiac Rehabilitation.

Konstantina-Helen Tsarapatsani, Vasilis D Tsakanikas, Boris Schmitz

    IEEE Journal of Biomedical and Health Informatics
    |December 8, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Machine learning models can predict cardiac rehabilitation (CR) effectiveness and adherence. Random Forest and Logistic Regression models show promising results, enabling personalized patient care and improved outcomes in CR programs.

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    Area of Science:

    • Cardiology
    • Biomedical Informatics
    • Data Science

    Background:

    • Cardiac rehabilitation (CR) is crucial for post-cardiac event recovery.
    • Variability in CR program effectiveness and patient adherence can lead to suboptimal outcomes.
    • Predictive models are needed to personalize CR interventions.

    Purpose of the Study:

    • To develop and evaluate machine learning (ML) models for predicting CR program effectiveness.
    • To develop and evaluate ML models for predicting CR program adherence.
    • To create a system for personalized visualization of patient response to CR.

    Main Methods:

    • Retrospective cohort study of 1448 participants from a Spanish CR unit.
    • Data preprocessing including cleaning, normalization, imputation, and feature selection.
    • Development and validation of ML models (Random Forest, Logistic Regression) using cross-validation, evaluated by AUC, specificity, sensitivity, and balanced accuracy.

    Main Results:

    • Random Forest model achieved an AUC of 0.789 for predicting CR effectiveness.
    • Logistic Regression model achieved an AUC of 0.757 for predicting CR adherence.
    • SHAP plots were used for variable analysis, and a two-dimensional scoring system was developed.

    Conclusions:

    • ML models can effectively predict CR program effectiveness and adherence.
    • Personalized prediction enables tailored patient management and potentially improved CR outcomes.
    • The developed scoring system offers a novel approach to visualize patient-specific CR response.