Paper Title
Heart disease prediction by Machine Learning
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Registration ID: IJNRD_305047
Published ID: IJNRD2504391
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Keywords
Heart Disease , Machine Learning, Feature Selection, Dimensionality Reduction, SVN, NN, LR .RF.
Abstract
Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine learning (ML), a rapidly evolving field within artificial intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines key ML algorithms, datasets, and evaluation metrics utilized in heart disease prediction. It explores the role of supervised learning techniques, such as logistic regression and decision trees, alongside advanced approaches like deep learning. Key datasets, including the Cleveland Heart Disease Dataset, have been instrumental in developing predictive models. However, challenges such as data quality, interpretability, and generalizability persist. Integrating wearable technologies, enhancing model explainability, and adopting privacy-preserving methods like federated learning are essential for advancing ML in cardiology. This paper provides a roadmap for researchers to address current gaps and foster the development of efficient, real-time healthcare solutions.This review emphasizes the importance of integrating ML with wearable technologies, enhancing explainability, and adopting federated learning to overcome these limitations. By addressing these challenges, ML-based systems could revolutionize heart disease management, paving the way for personalized, real-time, and accurate healthcare solutions. This study aims to provide researchers and clinicians with insights into current trends, gaps, and future directions in the application of ML for heart disease prediction.
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How To Cite (APA)
Dev kaushik, Saksham jain, Sumit Kumar, & Punit mittal (April-2025). Heart disease prediction by Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), d590-d594. https://ijnrd.org/papers/IJNRD2504391.pdf
Issue
Volume 10 Issue 4, April-2025
Pages : d590-d594
Other Publication Details
Paper Reg. ID: IJNRD_305047
Published Paper Id: IJNRD2504391
Research Area: Science and Technology
Author Type: Indian Author
Country: MEERUT, Uttar Pradersh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2504391.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504391
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