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基于机器学习的预测和干预心血管闭塞的干预措施.

Anvin Thomas1, Rejath Jose1, Faiz Syed1

  • 1College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY, USA.

Technology and health care : official journal of the European Society for Engineering and Medicine
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机器学习通过分析心血管封闭数据来增强心脏病发作和中风的预测. 这种方法有助于早期干预和风险分层,以改善患者的治疗结果.

关键词:
机器学习是机器学习.心血管疾病心血管疾病在临床决策过程中.心脏病发作是因为心脏病发作.预测建模预测建模有关风险因素的风险因素.一次性中风中风中风中风中风

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科学领域:

  • 心血管医学 心血管医学
  • 生物医学数据科学是生物医学数据科学.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 心血管疾病 (CVD) 是全球主要的死亡原因,心脏病发作和中风对健康构成重大挑战.
  • 心血管封闭或血管堵塞是导致心血管疾病死亡的关键因素.
  • 准确和早期的诊断和管理对于改善心血管疾病患者的治疗结果至关重要.

研究的目的:

  • 利用机器学习 (ML) 来改善心血管封闭的预测和管理.
  • 通过先进的ML干预来减少心脏病发作,中风和其他相关健康问题的发生率.
  • 为心血管事件制定更准确和及时的诊断和管理策略.

主要方法:

  • 分析各种数据集,使用各种ML算法来预测心脏病发作和中风.
  • 将ML模型性能进行比较,以确定最准确和可靠的预测器.
  • 根据预测的风险水平对个人进行分类,并检查关键相关特征.
  • 利用PyCaret的分类模块和分层交叉验证来进行强大的模型开发和评估.

主要成果:

  • 机器学习显著提高了对心脏病发作和中风的预测准确度.
  • 确定了与心血管事件发生率相关的关键特征.
  • 通过ML预测证明了通过ML预测更早,更精确的医疗干预的潜力.

结论:

  • 基于ML的风险分层和可修改因素的识别使得预防性心血管护理成为可能.
  • 将ML模型有效地整合到临床实践中需要解决挑战,并确保医疗保健专业人员的解释.
  • 该研究旨在减少危及生命的心血管事件,并改善患者长期健康轨迹.