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The most significant and important concern in the healthcare industry is disease diagnosis. Early disease detection can save lives. The medical industry can greatly benefit from machine learning categorization approaches by offering rapid and accurate syndrome diagnosis. Therefore, this approach saves time for both doctors and patients. Currently, the leading cause of death worldwide is cardiac disease, which is one of the most difficult illnesses to treat, and illnesses must be identified. Here, we provide an overview of machine learning classification techniques that have been suggested to aid medical practitioners in the diagnosis of cardiovascular risk. We begin by providing a general review of machine learning and succinct explanations of the most popular classification algorithms for predicting cardiovascular risk. Then, we examine research studies that are representative of this field’s use of machine learning classification approaches. Here, we also provided a comprehensive tabular comparison of the papers surveyed.

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Analysis and Comparison of Cardiovascular Risk Prediction Using Machine Learning Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Sudipta Hazra,
  • Siddhartha Chatterjee,
  • Rituparna Mondal,
  • Anwesa Naskar

摘要

The most significant and important concern in the healthcare industry is disease diagnosis. Early disease detection can save lives. The medical industry can greatly benefit from machine learning categorization approaches by offering rapid and accurate syndrome diagnosis. Therefore, this approach saves time for both doctors and patients. Currently, the leading cause of death worldwide is cardiac disease, which is one of the most difficult illnesses to treat, and illnesses must be identified. Here, we provide an overview of machine learning classification techniques that have been suggested to aid medical practitioners in the diagnosis of cardiovascular risk. We begin by providing a general review of machine learning and succinct explanations of the most popular classification algorithms for predicting cardiovascular risk. Then, we examine research studies that are representative of this field’s use of machine learning classification approaches. Here, we also provided a comprehensive tabular comparison of the papers surveyed.