Related Experiment Video
Updated: May 8, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
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.
Insights
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.
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.
Abstract:
The medical community demands accurate predictive models for early heart disease diagnosis because heart disease remains a significant worldwide health concern. Deep learning research presents a predictive system for heart disease that uses K-mode clustering to optimize data preparation and implements Harris Hawks Optimization (HHO) for essential feature selection. The Cardiovascular Disease dataset of 70,000 patient records with many clinical parameters was used to develop model training and validation. The accuracy of cardiovascular disease prediction depends on neural networks and other deep learning architectures which analyze patient risk factors to determine disease development. The model operates efficiently using precision, recall, accuracy and the AUC score to evaluate its performance while utilizing the ROC curve. The developed feature selection method applying HHO increases model efficiency while maintaining prediction capabilities by eliminating unneeded features. The Gated Recurrent Unit (GRU) model achieved the highest accuracy of 88.03% among all the tested frameworks. Deep Learning methodologies integrated with advanced feature selection demonstrate high effectiveness in early detection of cardiovascular disease. It leads to efficient diagnostic solutions for health applications that scale across various systems.
