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通过堆叠组合和基于MCDM的排名来提高心脏病预测:一种优化的RST-ML方法

T Ashika¹, G Hannah Grace¹

¹Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai, Chennai, India.

Frontiers in digital health

|July 4, 2025

概括

这项研究引入了一个优化粗体集合理论-机器学习框架,用于准确预测心脏病. 这种新的方法提高了诊断准确度,并在多种健康状况中展示了可扩展性.

科学领域:

计算生物学和生物信息学
机器学习在医疗保健中的应用
用于医学诊断的数据科学.

背景情况:

心血管疾病 (CVD) 仍然是全球首要的死亡原因,这强调了对先进诊断工具的需求.
现有的诊断模型经常面临高维度和特征冗余的挑战.
将机器学习与数据减少技术相结合,为提高预测准确性提供了一个有希望的途径.

研究的目的:

为心脏病 (HD) 预测开发和评估一个优化的粗集理论-机器学习 (RST-ML) 框架.
通过堆叠组合模型和多标准决策来提高诊断准确度和减少过度拟合.
评估框架的可扩展性和对各种健康数据集的概括能力.

主要方法:

使用粗集合理论 (RST) 进行特征选择,以最大限度地减少数据的维度.
开发五个堆叠组合模型,集成九个机器学习分类器.
模型排名使用以理想解决方案相似度为优先顺序的技术 (TOPSIS) 与平均排名错误纠正 (MEREC) 权重.
使用 GridSearchCV 的超参数优化,确定 XGBoost (XG) 为最佳分类器.
对心脏病,慢性病 (CKD),肥胖和乳腺癌数据集的评估.
可解释AI (XAI) 的应用用于特征重要性分析.

主要成果:

使用XGBoost的Stack-4组合模型实现了最高的预测准确度.
可解释AI (XAI) 技术成功阐明了影响诊断预测的关键特征.
RST-ML框架在多个数据集中显示出强大的性能,包括CKD和乳腺癌.

结论:

拟议的RST-ML框架显著提高了心脏病预测的准确性.
该框架具有强大的可扩展性和通用性,在各种健康状况中对及时诊断有效.
这种方法为各种临床环境中的医学诊断提供了强大而适应性的解决方案.

关键词:

相关性分析的相关性分析.网格搜索CVCV 网格搜索机器学习是机器学习.采用多个标准的决策.一个粗略的集合理论.堆叠分类器堆叠分类器

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