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可解释性冠状动脉疾病预测模型基于AutoML框架中的AutoGluon

Jianghong Wang¹, Qiang Xue¹, Chris W J Zhang²

¹Faculty of Information Engineering and Automation, Center for Precision Medicine, Yan'an Hospital of Kunming City & Kunming University of Science and Technology, Kunming, China.

Frontiers in cardiovascular medicine

|July 16, 2024

概括

自动机器学习 (AutoML) 能够高准确性地有效预测冠状动脉疾病 (CAD). 像SHAP这样的可解释的人工智能方法可以确保在心血管医学中临床使用的模型透明度.

科学领域:

心血管医学心血管医学
人工智能的人工智能
机器学习机器学习

背景情况:

冠状动脉疾病 (CAD) 的临床诊断需要准确的预测模型.
传统的模型开发可能是复杂和耗时的.
在医疗决策中需要可解释的人工智能.

研究的目的:

应用自动机器学习 (AutoML) 进行可解释的CAD预测模型.
支持心血管医学中的临床诊断.
评估AutoML在这个领域的可行性和性能.

主要方法:

使用了来自五个公共CAD相关来源的综合数据集.
使用AutoGluon AutoML框架开发了一个集合模型.
使用夏普利添加式扩展 (SHAP) 解释模型预测.

主要成果:

在CAD预测中,AutoGluon整体模型的表现优于个别基线模型.
实现了高性能指标:0.9167准确度和0.9562 AUC (4倍交叉背包).
SHAP分析提供了特征的重要性,并解释了模型预测.

结论:

自动ML对于心血管疾病的预测是可行的和有效的.
自动ML简化了模型构建,并提高了预测准确性.
SHAP集成提高了模型的透明度和临床采用可信度.

关键词:

在 AutoGluon 中使用.自动机器学习自动化机器学习莎普利的添加式解释心脏病疾病心脏病疾病预测模型预测模型

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