Machine Learning-Based Approach for Heart Disease Prediction

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Dev Kaushik,

  • Saksham Jain,

  • Sumit Kumar,

  • Nadeem Anwar,

  1. Student, Computer Science & Information Technology, Meerut Institute of Engineering & Technology, Meerut, Uttar Pradesh, India
  2. Student, Computer science & Information Technology, Meerut Institute of Engineering & Technology, Meerut, Uttar Pradesh, India
  3. Student, Computer Science & Information Technology, Meerut Institute of Engineering & Technology, Meerut, Uttar Pradesh, India
  4. Assistant Professor, Information Technology, Meerut Institute of Engineering & Technology, Meerut, Uttar Pradesh, India

Abstract

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 Heart Disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic regression, and even more Enhanced techniques, for example, 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 information about the current status, gaps, and future directions in the integration of machine learning in Heart Disease Prediction.

Keywords: Heart disease, machine learning, feature selection, dimensionality reduction, SVN, NN, LR, RF.

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Dev Kaushik, Saksham Jain, Sumit Kumar, Nadeem Anwar. Machine Learning-Based Approach for Heart Disease Prediction. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-.
How to cite this URL:
Dev Kaushik, Saksham Jain, Sumit Kumar, Nadeem Anwar. Machine Learning-Based Approach for Heart Disease Prediction. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0



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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 03/03/2025
Accepted 11/04/2025
Published 19/04/2025
Publication Time 47 Days
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