一种基于人工神经网络的高效优化技术,用于早期预测冠心病:综合分析
Subhash Mondal1,2, Ranjan Maity1, Amitava Nag3
1Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, 783370, India.
Scientific reports
|February 9, 2025
概括
这项研究开发了一种人工神经网络 (ANN) 系统,使用弗雷明汉心脏研究数据预测冠状动脉疾病 (CHD) 风险. 先进的模型实现了96.25%的准确性,为早期检测提供了有价值的工具.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
背景情况:
- 冠心病 (CHD) 是全球主要的死亡原因.
- 准确的风险预测对于有效的CHD管理至关重要.
- 现有的决策支持系统需要加强,以改善冠状动脉疾病风险评估.
研究的目的:
- 开发一个使用人工神经网络 (ANN) 预测心脏病风险的智能医疗系统.
- 利用弗雷明汉心脏研究 (FHS) 数据集进行模型培训和验证.
- 通过解决数据不平衡和模型过拟合,提高预测准确度.
主要方法:
- 实施了一种具有四个隐藏密集层和脱落规范化的ANN模型.
- 在Adam,SGD,RMSProp和AdaDelta优化器中使用了ReLU和LeakyReLU激活功能.
- 应用了SMOTE,SMOTETomek,SMOTEENN和两阶段采样方法进行数据预处理.
主要成果:
- 亚当优化器,ReLU激活和SMOTEENN/SMOTETomek两阶段采样的组合实现了卓越的性能.
- 该模型实现了96.25%的验证准确率和0.98.98%的召回率.
- 拟议的系统在CHD风险预测方面表现优于现有的方法.
结论:
- 开发的基于ANN的系统在预测心脏病风险方面表现出高效.
- 先进的预处理和优化模型参数的综合方法显著提高了预测准确性.
- 这种智能系统为心血管疾病管理中的现代医疗保健决策提供了有希望的进步.
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