Journal of Rare Cardiovascular Diseases
ISSN: 2299-3711 (Print)
e-ISSN: 2300-5505 (Online)
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Advanced Machine Learning and Deep Learning Approaches with Optimization Techniques for Early Heart Disease Detection Using Recent Healthcare Datasets
Dr. S. Madhan Mohan
Dr. M. Muthamizharasan
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1
Assistant Professor, Department of Computer Applications, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, India. Email: nsmadhanmohan@gmail.com
2
Associate Professor & Head, Department of Computer Science, A.V.C. College (Autonomous), Mannampandal, Mayiladuthurai, Tamil Nadu, India.
Received: 2025-08-10
Revised: 2025-09-14
Accepted: 2025-10-10
Published: 2025-10-20
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Abstract
Heart disease remains one of the leading causes of mortality worldwide, making early diagnosis essential for improving patient outcomes and reducing healthcare costs. Recent advancements in artificial intelligence have enabled the development of intelligent systems capable of assisting medical professionals in the accurate prediction of cardiovascular diseases. This study presents an optimized framework that integrates advanced Machine Learning (ML) and Deep Learning (DL) approaches for the early detection of heart disease using recent healthcare datasets. The proposed methodology involves data preprocessing, feature selection, and optimization techniques to improve model performance and prediction accuracy. Various ML algorithms, including Decision Tree, Random Forest, Support Vector Machine, and XGBoost, are compared with DL models such as Artificial Neural Networks and Deep Neural Networks. Optimization methods are employed to enhance parameter tuning, reduce computational complexity, and improve classification efficiency. Experimental results demonstrate that the optimized models achieve superior performance in terms of accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The findings indicate that the integration of optimization techniques with ML and DL models can significantly enhance the reliability and effectiveness of heart disease prediction systems. This research contributes to the development of intelligent healthcare solutions that support early diagnosis and timely clinical decision-making.
Keywords
Heart Disease Prediction, Machine Learning, Deep Learning, Optimization Techniques, Healthcare Datasets, and Early Disease Detection
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Keywords
Classification of Rare Cardiovascular Diseases
anticoagulation
atrial fibrillation
atrial septal defect
cardiomyopathy
computed tomography
congenital heart disease
echocardiography
electrocardiogram
electrocardiography
heart failure
implantable cardioverter‑defibrillator
magnetic resonance imaging
pregnancy
pulmonary arterial hypertension
pulmonary hypertension
rare cardiovascular disease
rare disease
right heart catheterization
right ventricular failure
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