一种混合堆叠组合和基于Kernel SHAP的模型,用于智能心脏图形学分类和可解释性
Junyuan Feng1, Jincheng Liang1, Zihan Qiang2
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
BMC medical informatics and decision making
|November 29, 2023
概括
这项研究引入了一种混合机器学习模型用于心脏图形学 (CTG) 分类,实现高精度和可解释性的胎儿健康评估. 该模型确定了关键决定因素,如异常的短期变化和加速,以改善产前临床应用.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 产科 产科 产科 产科 产科
背景情况:
- 智能心脏病学 (CTG) 分类有助于评估胎儿健康.
- 复杂的机器学习 (ML) 模型提供了高性能,但缺乏可解释性,阻碍了临床采用.
- 在产前护理中基于ML的CTG分类中,在平衡准确性和可解释性方面存在差距.
研究的目的:
- 为了提高CTG分类性能和预测解释性.
- 开发一种混合模型,将堆叠组合方法与特征分析集成在一起.
- 用公共和私人数据集验证模型的有效性和可解释性.
主要方法:
- 使用支持矢量机器 (SVM),极端梯度增强 (XGB) 和随机森林 (RF) 作为基本学习器构建了一个堆叠的集合分类器.
- 一个反向传播 (BP) 的元学习器集成了CTG功能与基础学习器输出.
- 核心SHapley添加式解释 (SHAP) 框架用于特征贡献分析.
主要成果:
- 混合模型实现了高精度 (0.9539公众,0.9201私人) 和F1得分 (0.9249公众,0.8926私人) 通过十倍的交叉验证.
- 确定CTG分类的关键决定因素是加速 (AC) 和异常短期变化 (ASTV) 的时间百分比.
- 增加的ASTV与更高的异常概率相关,而增加的AC与更高的正常状态概率相关.
结论:
- 拟议的混合模型证明了智能胎儿监测的强大分类性能.
- 该模型提供了合理的解释性,识别了影响胎儿状态预测的关键特征.
- 这种方法解决了准确性-解释性权衡问题,促进了潜在的临床整合.
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