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一种基于UCI存储库数据库的积极学习机器技术预测心血管疾病

Saravanan Srinivasan¹, Subathra Gunasekaran², Sandeep Kumar Mathivanan³

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India.

Scientific reports

|August 21, 2023

概括

机器学习模型可以准确预测心血管疾病 (CHD). 学习矢量量化实现了98.7%的准确性,为早期干预和改善患者结果提供了潜力.

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:

心脏病是全球主要的死亡原因,这给诊断带来了挑战.
机器学习 (ML) 为分析医疗保健数据以预测心脏病提供了一种强大的方法.
之前的研究强调了在医学研究中广泛使用ML来预测心脏病.

研究的目的:

通过使用ML来确定提高心脏病预测准确性的关键特征.
开发和评估用于预测心血管疾病 (CHD) 的ML模型.

主要方法:

利用八个不同的ML分类器来分析临床数据.
实现了各种功能组合与已建立的分类算法.
应用神经网络模型,包括天真贝叶斯函数和辐射基函数.

主要成果:

纯粹的贝叶斯函数获得了94.78%的准确率,辐射基函数获得了90.78%的准确率.
学习矢量量化证明了最高的预测准确率为98.7%.
确定了关键特征,可以显著提高预测模型的准确性.

结论:

ML,特别是学习载体量化,在预测心血管性心脏病方面表现出很高的有效性.
准确的CHD预测可以促进早期干预,个性化医疗和高效的医疗保健资源分配.
在ML的进步有助于减少心血管疾病的公共卫生负担.

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