优化混合RNN-GRU模型用于心血管疾病的预测诊断
Gaurav Kumar1, Neeraj Varshney1
1Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India.
Biomedical physics & engineering express
|September 30, 2025
概括
一种结合重复神经网络 (RNN) 和门式重复单元 (GRU) 的新型混合深度学习模型显著提高了心脏病风险预测的准确性. 这种先进的模型为早期心脏病检测和临床决策提供了卓越的性能.
科学领域:
- 心脏病学和人工智能的人工智能
- 生物医学工程 生物医学工程
- 在医疗保健中的数据科学.
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,印度面临着相当大的负担.
- 准确和有效的心脏病风险预测对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和评估混合深度学习模型,以提高心脏病风险预测.
- 提高识别心血管疾病风险的个人的准确性和效率.
主要方法:
- 在918个样本的数据集上使用混合反复神经网络 (RNN) -Gated Recurrent Unit (GRU) 深度学习模型.
- 应用数据预处理,包括异常值校正 (IQR),规范化和合成少数超样本技术 (SMOTE) 进行数据集平衡.
- 雇佣的GridSearchCV具有10倍的交叉验证,用于模型微调.
主要成果:
- 混合RNN-GRU模型实现了卓越的性能,超过了单独的RNN和GRU模型.
- 实现了高精度 (99.6%),F1得分 (99.6%),精度 (99.6%) 和回忆 (99%).
- 演示的性能明显高于之前报告的准确率,分别为87%和97%.
结论:
- 混合RNN-GRU模型有效地从心脏信号中提取时间特征,这对于准确的风险预测至关重要.
- 该模型显示了在早期和精确的心脏病检测中增强临床决策的巨大潜力.
- 强调了强大的数据预处理技术在开发有效预测模型中的重要性.
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