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<p>Cardiovascular diseases, especially myocardial infarction (MI), are an important and up-trending public health challenge in the South Asian population. With urbanization and economic development, there has been a rise in obesity, dyslipidemia, diabetes mellitus, and hypertension in these regions, which, combined with genetic predisposition, create a unique cardiovascular risk profile among South Asians. Traditional risk assessment tools often underestimate the cardiovascular risk in South Asians due to a lack of phenotypic representation in their development. In this review, we explore the risk factors for MI in South Asians and highlight the potential role of machine learning (ML) and deep learning (DL) in enhancing diagnostic and predictive accuracy. These ML algorithms, including convolutional neural networks (CNNs) and transformer-based models, show potential in analyzing complex information from clinical characteristics, electrocardiograms (ECG), and cardiac biomarkers while integrating multimodal data. We also explore the challenges in accessing high-quality datasets and enabling applicability in clinical settings. We believe that future research should focus on developing comprehensive cardiovascular risk scores that incorporate South Asian-specific risk factors and leverage advanced ML models to enhance risk prediction, diagnosis, and management.</p>

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Addressing myocardial infarction in South-Asian populations: risk factors and machine learning approaches

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月3日

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  • Rick Rejeleene,
  • Vignesh Chidambaram,
  • Meena Chatrathi,
  • Amudha Kumar,
  • Emily Lu,
  • Ryan Pohlkamp,
  • Mukunthan Murthi,
  • Nitesh Gautam,
  • Subhi Al’Aref,
  • Xiaowei Xu,
  • Jawahar L. Mehta

摘要

Cardiovascular diseases, especially myocardial infarction (MI), are an important and up-trending public health challenge in the South Asian population. With urbanization and economic development, there has been a rise in obesity, dyslipidemia, diabetes mellitus, and hypertension in these regions, which, combined with genetic predisposition, create a unique cardiovascular risk profile among South Asians. Traditional risk assessment tools often underestimate the cardiovascular risk in South Asians due to a lack of phenotypic representation in their development. In this review, we explore the risk factors for MI in South Asians and highlight the potential role of machine learning (ML) and deep learning (DL) in enhancing diagnostic and predictive accuracy. These ML algorithms, including convolutional neural networks (CNNs) and transformer-based models, show potential in analyzing complex information from clinical characteristics, electrocardiograms (ECG), and cardiac biomarkers while integrating multimodal data. We also explore the challenges in accessing high-quality datasets and enabling applicability in clinical settings. We believe that future research should focus on developing comprehensive cardiovascular risk scores that incorporate South Asian-specific risk factors and leverage advanced ML models to enhance risk prediction, diagnosis, and management.