基于深度学习多尺度卷积增强的Swin变压器模型的心脏病风险预测
Shengli Li1, Zhangyi Shen1,2, Qiqi Song1
1School of Computer and Information, Anhui Normal University, Wuhu, China.
Computer methods in biomechanics and biomedical engineering
|September 6, 2025
概括
这项研究引入了一种用于预测心脏病风险的新人工智能模型. 多尺度卷积增强的Swin变压器 (MSCST) 在早期检测中显示出更高的准确性.
科学领域:
- 心脏病学
- 人工智能
- 机器学习
背景情况:
- 需要先进的诊断工具.
- 早期预测心脏病对于有效的干预和改善患者结果至关重要.
研究的目的:
- 开发和评估一种新的深度学习模型,用于准确评估心脏病风险.
- 在心血管风险预测中增强人工智能模型的可解释性.
主要方法:
- 开发了一个多尺度卷积增强的Swin变压器 (MSCST) 模型.
- 该模型使用多分支卷积网络,用于特征提取.
- 斯温变压器模块通过自我注意来整合全球和本地信息,并使用SHAP分析进行解释.
主要成果:
- 在克利夫兰心脏病数据集中,MSCST模型的准确度为89. 42%,AUC为0. 8908.
- 性能超越了传统的机器学习和现有的深度学习方法来预测心脏病.
结论:
- 通过MSCST模型,可以对心脏病风险进行准确和可解释的评估.
- 这种人工智能驱动的方法为早期心血管疾病检测提供了有前途的进步.
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