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一个可解释的混合框架,用于早期检测心血管疾病,使用分类提升和蜜蜂算法

Jayanta Sen¹, Sweta Bhattacharya²

¹School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India. jayanta.sen@vit.ac.in.

Scientific reports

|December 13, 2025

概括

一个新的混合机器学习 (ML) 模型准确地检测心血管疾病 (CVD),并为向治疗提供可解释的结果. 这种先进的框架增强了早期疾病检测和治疗计划.

科学领域:

心血管健康的心血管健康
机器学习应用程序机器学习应用程序
医学中的人工智能

背景情况:

心血管疾病 (CVD) 是全球主要的死亡原因.
早期发现心血管疾病对于有效的患者管理至关重要.
传统的机器学习 (ML) 模型用于疾病预测往往缺乏透明度 ("黑盒").

研究的目的:

为准确的心血管疾病检测开发一个可解释的ML框架.
为针对性治疗提供对影响心血管疾病发生的因素的见解.
通过透明的AI预测来增强临床决策.

主要方法:

使用了弗雷明汉心血管疾病 (CVD) 数据集.
应用随机过量抽样 (RO) 用于数据平衡和Min-Max缩放用于规范化.
开发了一个混合ML模型,结合了分类提升 (CatBoost) 和BEE算法.
实现可解释的人工智能 (XAI) 技术 (LIME,SHAP) 进行解释.

主要成果:

混合ML模型实现了98.04%的准确性,97.09%的精度,98.96%的回忆,98.02%的F1得分和97.16%的特异性.
该模型与现有的最先进的算法相比,表现出更高的性能.
通过XAI技术确定了导致心血管疾病发生的关键属性.

结论:

拟议的混合ML模型为心血管疾病检测提供了一个高度准确和可解释的解决方案.
可解释的人工智能为医疗保健提供者提供了宝贵的见解,促进及时准确的治疗决策.
这一框架有可能显著改善心血管疾病管理和患者的治疗结果.

关键词:

蜜蜂蜜蜂是一种心血管疾病是什么心血管疾病在 CatBoost 中使用 CatBoost.可解释的人工智能机器学习机器学习

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