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通过机器学习和统计方法彻底改变了心血管疾病的分类.

Tapan Kumar Behera1, Siddhartha Sathia2, Sibarama Panigrahi3

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机器学习 (ML) 模型为心血管疾病 (CVD) 提供了具有成本效益的数字诊断. 额外树分类器在准确性和精确性方面表现出色,而XGBoost在CVD分类的回忆,kappa和F1分数方面处于领先地位.

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心血管疾病 (CVD) 是一种心血管疾病.高斯的天真贝耶斯 (GNB) 的K-最近的邻居 (KNN)适应性增强 (ADB) 是一种增强方式.人工智能 (AI) 是一种人工智能.人工神经网络 (ANN) 是一个人工神经网络.包装分类器 (BC) 包装分类器决策树 (DT) 是指一个决策树.额外的树木 (TC)额外的树木 (ETC)极端梯度提升 (XGBoost) 是一种极端梯度提升.极端梯度提升随机森林 (XGBRF) 的结果梯度增强 (GB) 是一个线性支向量的分类器 (LSVC)逻辑回归 (LR) 是一种逻辑回归.机器学习 (ML) 是指机器学习.多层感知子 (MLP) 是一种多层感知子.被动攻击性分类器 (PAC)随机森林 (RF) 是一个随机的森林.山脊分类器 (RC) 的使用随机梯度下降 (SGD) 是指随机梯度的下降.支持向量分类器 (SVC)投票分类器 (VC) 投票分类器

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 心血管疾病 (CVD) 涵盖了一系列的心脏和血管疾病.
  • 传统的CVD诊断依赖于昂贵且耗时的专家临床评估.
  • 新兴的机器学习 (ML) 和统计技术使成本有效的数字心血管疾病诊断成为可能.

研究的目的:

  • 使用 19 种机器学习 (ML) 模型对心血管疾病 (CVD) 进行分类.
  • 为了评估和排名ML模型对CVD分类的性能.
  • 使用基准数据集评估ML模型的效率和可靠性.

主要方法:

  • 使用了19个ML模型进行CVD分类.
  • 使用来自Kaggle和UCI存储库的两个基准CVD数据集.
  • 对每个模型和数据集进行了50次模拟,使用非参数统计测试.

主要成果:

  • 额外树分类器在统计学上表现出卓越的准确性和精度.
  • 极端梯度提升 (XGBoost) 分类器在统计学上取得了更高的回忆,卡帕和F1分数.
  • 在召回性能方面,XGBRF分类器排名第二.

结论:

  • ML模型为心血管疾病诊断提供了一个可行的替代方案.
  • 特定的ML模型在CVD分类的不同性能指标中显示出不同的优势.
  • 统计测试证实了评估的ML模型之间的显著性能差异.