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使用机器学习算法预测心脏病风险.

Albert Alexander Stonier1, Rakesh Krishna Gorantla2, K Manoj2

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概括
此摘要是机器生成的。

这项研究开发了一个机器学习 (ML) 系统来预测心脏病发作风险. 随机森林算法实现了88.52%的准确性,为早期心血管疾病检测提供了一个有前途的工具.

关键词:
在 KNN KNN 标签上.心脏病是一种心脏病.决策树是一个决策树.心脏病发作是因为心脏病发作.机器学习是机器学习.一个天真的贝叶斯.神经网络的神经网络的神经网络预测 预测 预测 预测随机的森林随机的森林回归模型是一种回归模型.支持矢量机器的支持矢量机器.

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科学领域:

  • 心脏病学 心脏病学
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 心脏病发作,通常是由冠状动脉疾病引起的,是导致死亡的主要原因.
  • 早期发现和管理心脏病风险对于预防和降低医疗保健成本至关重要.
  • 机器学习 (ML) 对预测医疗保健中的疾病发生变得越来越重要.

研究的目的:

  • 开发用于心脏病发作风险评估的预测系统.
  • 分析各种数据来源,包括电子健康记录和临床报告.
  • 利用ML提高心血管健康的诊断能力.

主要方法:

  • 应用各种机器学习算法进行预测分析.
  • 随机森林,回归模型,K-近邻归算 (KNN) 和天真贝叶斯算法的比较.
  • 利用电子健康记录和临床诊断报告中的患者数据.

主要成果:

  • 随机森林算法在预测心脏病发作风险方面表现出卓越的性能.
  • 随机森林模型的准确度为88.52%.
  • 对比分析强调了随机森林的有效性相对于其他经过测试的ML方法.

结论:

  • 机器学习,特别是随机森林算法,显示了准确预测心脏病发作风险的巨大潜力.
  • 这种方法可以彻底改变心血管疾病的诊断和治疗.
  • 早期预测系统可以提高患者的治疗结果,并优化医疗资源的分配.