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Related Experiment Videos

Explainable Hybrid Deep Learning Framework for Cardiovascular Disease Prediction and Clinical Decision Support.

Bhaskar Adepu1, T Archana1

  • 1Department of Computer Science and Engineering, Kakatiya University, Warangal, Telangana, India.

Cardiovascular & Hematological Disorders Drug Targets
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...

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A new hybrid AI model, HCVDNet, accurately predicts cardiovascular disease risk by combining ensemble learning and deep learning. Explainable AI techniques provide clear insights, enhancing physician confidence and enabling real-time clinical deployment.

Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Disease Prediction

Background:

  • Cardiovascular disease (CVD) poses a significant global public health challenge.
  • Machine learning (ML) models offer potential for early CVD identification and risk prediction.
  • Existing models often lack interpretability, hindering clinical adoption.

Purpose of the Study:

  • To propose HCVDNet, a hybrid ML-deep learning model for enhanced CVD risk prediction.
  • To improve the accuracy and interpretability of AI-driven CVD diagnostics.
  • To provide clinicians with clear, actionable explanations for AI predictions.

Main Methods:

  • HCVDNet integrates ensemble methods (XGBoost, CatBoost, Random Forest) with deep learning (CNNs, LSTM).
  • Post-hoc explainable AI techniques (SHAP, LIME) are incorporated for global and instance-level explanations.
Keywords:
Cardiovascular disease (CVD)LIMESHAPexplainable AI (XAI)health information systemshybrid deep learningmachine learning ensemble

Related Experiment Videos

  • Experiments utilize cardiovascular datasets from UCI and Kaggle repositories.

    • Main Results:

    • HCVDNet achieved superior performance over baseline models, with 96.94% accuracy and 98.72% AUC.
    • Explainable AI methods confirmed clinically relevant feature patterns in model outputs.
    • The model demonstrated a low Expected Calibration Error (ECE) of 0.021.

    Conclusions:

    • Combining ensemble learning and deep temporal modeling significantly boosts predictive accuracy and interpretability.
    • Explainable AI enhances physician trust in AI-assisted decision-making for CVD risk.
    • HCVDNet offers a high-performance, interpretable, FHIR-compliant framework for real-time clinical application.