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一个可解释的机器学习 (XAI) 框架,以增强类型的心血管疾病诊断和预后

K Adalarasu¹, B Raghavan¹, B Madhavan¹

¹School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, 613401, India.

Physical and engineering sciences in medicine

|October 1, 2025

概括

心血管疾病 (CVD) 是导致死亡的主要原因. 这项研究使用心电图 (ECG) 数据开发了一个机器学习 (ML) 模型,通过支持矢量机 (SVM) 和可解释的人工智能 (XAI) 实现99.8%的准确性,用于早期心脏病诊断.

科学领域:

心脏病学和医疗信息学
机器学习在医疗保健中的应用

背景情况:

心血管疾病 (CVD) 占全球死亡人数的32%,其中85%归因于心肌梗塞和中风.
早期诊断心脏疾病对于降低死亡率和与心脏结构异常相关的风险至关重要.

研究的目的:

利用心电图 (ECG) 数据开发一个基于数据的模型,用于早期诊断心脏疾病.
通过及时的医疗干预来降低心脏结构异常的风险.

主要方法:

从PhysioNet提取了心血管和标准心电图数据集,包括正常的心脏功能和各种心律失常.
预处理了心电图数据,提取了特征和衍生特征 (例如RR间隔,RMSSD,SDDN),并应用了可解释的人工智能 (XAI) 来进行特征贡献分析.
实施和评估机器学习 (ML) 算法,包括使用十倍交叉验证,准确性和回忆指标的Ensemble (EN),Naive Bayes (NB) 和Support Vector Machine (SVM). 通过合成少数群体过量采样技术 (SMOTE) 解决了阶级不平衡.

主要成果:

支持矢量机 (SVM) 显示出卓越的性能,在所有特征和心电图波特征上达到99.5%的准确性,在衍生特征上达到77%.
在应用SMOTE后,SVM模型的准确性进一步提高到99.8%.
可解释的人工智能 (XAI) 技术提高了模型透明度和对特征贡献的理解.

结论:

机器学习模型,特别是SVM,可以有效地从心电图特征预测心血管疾病异常.
该研究强调了XAI在提高模型透明度和促进心脏病诊断临床采用方面的潜力.
未来的研究应该专注于改进心电图特征提取以实现实时心血管疾病预测.

关键词:

心血管疾病 (CVD) 是一种心血管疾病.混矩阵是一种混矩阵.电心电图 (ECG) 是一种心电图.机器学习 (ML) 是指机器学习.业绩表现业绩表现业绩表现表现沙普利添加剂的解释

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