Revolutionizing Cardiology: A Comprehensive Review of Machine Learning Techniques for Heart Disease Prediction

Mithlesh Kumar; Dr. Vandana Yadav

Revolutionizing Cardiology: A Comprehensive Review of Machine Learning Techniques for Heart Disease Prediction

Authors: Mithlesh Kumar, Dr. Vandana Yadav

Unique Paper ID: 171472
Volume: 11
Issue: 8
PageNo: 54-60

Keywords: cardiovascular diseases (CVD) Machine learning (ML) support vector machines (SVM) heart health management decision trees.

Abstract:
cardiovascular diseases (CVDs) remain a leading cause of global mortality, underscoring the urgent need for advanced diagnostic and predictive tools. This comprehensive review explores the transformative potential of machine learning (ML) techniques in cardiology, focusing on their application to heart disease prediction. By analyzing diverse datasets, ML models can uncover hidden patterns and correlations, offering unparalleled accuracy and efficiency compared to traditional methods. This study categorizes ML algorithms commonly employed in cardiology, such as support vector machines, decision trees, deep learning networks, and ensemble methods, while evaluating their performance, interpretability, and clinical integration. Furthermore, the review highlights the challenges of data heterogeneity, ethical considerations, and model transparency. Emphasizing the importance of feature selection, data preprocessing, and real-time monitoring systems, this work envisions a future where ML-driven tools become indispensable in proactive heart health management, empowering clinicians with reliable, scalable, and personalized solutions to mitigate CVD risks effectively.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 8
PageNo: 54-60

Revolutionizing Cardiology: A Comprehensive Review of Machine Learning Techniques for Heart Disease Prediction

Available:https://ijirt.org/Article?manuscript=171472

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