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January 9, 2026Open Access

Artificial Intelligence, particularly deep learning, improved the reliability and speed of ECG signal analysis for early detection of heart disease.

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Key result

Artificial Intelligence, particularly deep learning, improved the reliability and speed of ECG signal analysis for early detection of heart disease.

Authors

SRSungini RijhwaniSNS.S. Khora and Pagolu NavyaNSNettem Nithya Sree

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Overview

Observations demonstrate improved heart disease detection in populations using AI-based ECG analysis, suggesting enhanced patient outcomes.

Key Points

  • To evaluate the role of artificial intelligence in the early detection of heart disease through ECG signal analysis.
  • Analysis of ECG signals using machine learning and deep learning models.
  • Implementation of Random Forest and Support Vector Machines for arrhythmia and coronary disease classification.
  • Utilization of Convolutional Neural Networks for automatic feature extraction from cardiac data.
  • Application of explainable AI techniques like SHAP to enhance prediction transparency.
  • Machine learning models effectively classify different types of heart diseases.
  • Deep learning approaches auto-extract critical features from ECG data with high accuracy.
  • Explainable AI methods increase clinical confidence by clarifying prediction factors.
  • AI systems show improved performance with multimodal data integration.

Cite This Study

Rijhwani et al. (2026) studied this question. Artificial Intelligence, particularly deep learning, improved the reliability and speed of ECG signal analysis for early detection of heart disease.

synapsesocial.com/papers/696128f244c2cd6c68456c3chttps://doi.org/10.5281/zenodo.18149846
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