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Related Experiment Video

Updated: Jan 10, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

Enhancing cardiac disease prediction with explainable bidirectional LSTM.

Swati Lipsa1, Ranjan Kumar Dash1, Subhra Debdas2

  • 1School of Computer Sciences, Odisha University of Technology and Research, Bhubaneswar, 751029, Odisha, India.

Scientific Reports
|November 21, 2025
PubMed
Summary

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Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
316

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This study introduces novel machine learning models for early cardiac disorder detection using bidirectional LSTM and deep learning. These explainable models significantly improve accuracy and aid in annotating ECG reports for better patient outcomes.

Area of Science:

  • Cardiology
  • Machine Learning
  • Artificial Intelligence

Background:

  • Cardiovascular disorders are the leading global cause of mortality.
  • Early detection and classification of heart diseases are crucial for improving survival rates.
  • Accurate and explainable predictive models are needed for cardiac disorder diagnosis using machine learning.

Purpose of the Study:

  • To propose two novel machine learning models for cardiac disorder detection.
  • To implement binary and multi-label classification models for cardiac disease identification.
  • To enhance model explainability for better interpretation of ECG reports.

Main Methods:

  • Stacking bidirectional long short-term memory (LSTM) with deep learning for feature extraction and classification.
Keywords:
Bidirectional LSTMCardiovascular diseaseDeep learningECGExplainable AI

Related Experiment Videos

Last Updated: Jan 10, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
  • Training and validation of models on the PTB-XL dataset.
  • Utilizing SHAP (SHapley Additive exPlanations) for model explainability.

    • Main Results:

    • The proposed models demonstrated superior performance compared to state-of-the-art methods.
    • Achieved high accuracy, precision, f1-score, and recall in cardiac disorder classification.
    • Successfully enabled annotation of different diseases on ECG reports through explainability.

    Conclusions:

    • The developed bidirectional LSTM and deep learning models offer a powerful tool for accurate cardiac disorder detection.
    • Explainable AI (SHAP) enhances the clinical utility of predictive models for ECG interpretation.
    • This approach holds significant potential for improving cardiovascular disease diagnosis and patient care.