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Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
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Harris Hawks optimization based deep learning models for heart disease diagnosis.

Isabella S1, Lokeshraja B1, Nithish Narayan M1

  • 1School of Computing, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India.

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|November 3, 2025
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Summary

This study introduces a deep learning model for early heart disease prediction, utilizing K-mode clustering and Harris Hawks Optimization (HHO) for feature selection. The Gated Recurrent Unit (GRU) model achieved 88.03% accuracy, offering efficient diagnostic solutions.

Keywords:
Coronary heart diseaseDeep learningFeature selectionHarris hawks optimization (HHO)K-modes clusteringModel performance evaluationNeural networks

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Data Science

Background:

  • Heart disease is a major global health issue, necessitating accurate early diagnostic tools.
  • Predictive modeling using deep learning offers a promising avenue for improving cardiovascular disease detection.

Purpose of the Study:

  • To develop and validate a deep learning-based predictive system for early heart disease diagnosis.
  • To enhance model efficiency and accuracy through optimized data preparation and feature selection.

Main Methods:

  • Utilized the Cardiovascular Disease dataset (70,000 records).
  • Employed K-mode clustering for data optimization and Harris Hawks Optimization (HHO) for feature selection.
  • Implemented and evaluated various deep learning architectures, including Gated Recurrent Unit (GRU).

Main Results:

  • The GRU model achieved the highest prediction accuracy at 88.03%.
  • HHO-based feature selection improved model efficiency by removing redundant features.
  • Performance was evaluated using precision, recall, accuracy, and AUC score via ROC curve analysis.

Conclusions:

  • Deep learning methodologies, combined with advanced feature selection techniques like HHO, are highly effective for early cardiovascular disease detection.
  • The developed system provides an efficient diagnostic solution scalable for various health applications.