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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

  • 1Centre of Excellence in Natural Products and Therapeutics, Department of Biotechnology and Bioinformatics, Sambalpur University, Jyoti Vihar, Burla, Sambalpur, Odisha, India.

Journal of biopharmaceutical statistics
|November 25, 2024
PubMed
概括

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.

关键词:
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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