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  11. Machine Learning Approaches For Cardiovascular Disease Prediction: A Review

Machine learning approaches for cardiovascular disease prediction: A review

Siming Wan1, Feng Wan2, Xi-Jian Dai3

  • 1Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, 330006 Nanchang, China; Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, 330006 Nanchang, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.

Archives of Cardiovascular Diseases
|June 14, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning algorithms significantly improve early cardiovascular disease diagnosis. This review analyzes algorithms, implementation, and ethical considerations for AI in heart health.

Area of Science:

  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Diagnostics
  • Machine Learning Applications

Background:

  • Cardiovascular disease (CVD) remains a primary global cause of mortality and morbidity.
  • Conventional diagnostic methods have limitations in early disease detection.
  • Artificial intelligence (AI), specifically machine learning (ML), offers advanced diagnostic capabilities.

Purpose of the Study:

  • To conduct a systematic review of ML algorithms for early CVD diagnosis.
  • To analyze operational frameworks for integrating ML into clinical practice.
  • To critically evaluate regulatory compliance and ethical implications of AI in cardiology.

Main Methods:

  • Systematic review of recent literature on ML for cardiovascular disease prediction.
Keywords:
Cardiovascular diseaseClassificationFeature selectionMachine learning

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  • Analysis of comparative performance of different ML algorithms.
  • Examination of clinical implementation workflows and regulatory frameworks.

    • Main Results:

    • ML algorithms demonstrate enhanced accuracy in early CVD diagnosis compared to traditional methods.
    • Identified key operational frameworks for successful clinical integration of ML tools.
    • Highlighted significant regulatory and ethical challenges requiring careful consideration.

    Conclusions:

    • ML holds transformative potential for early cardiovascular disease detection and management.
    • Successful clinical implementation requires robust frameworks addressing operational, regulatory, and ethical aspects.
    • Future research should focus on refining algorithms and addressing implementation barriers.
    Prediction