一个基于混合堆叠的自编码器和支持矢量机器的专家系统,用于心力衰竭检测
Mian Muhammad Kamal1, Wahab Khan2, Qusai Y Shambour3
1School of Electronic and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn.
Scientific reports
|January 8, 2026
概括
这项研究介绍了一种新的混合专家系统,用于准确检测心力衰竭 (HF). 该系统使用先进的机器学习技术来显著提高诊断性能和效率.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
- 医疗保健中的人工智能
背景情况:
- 心力衰竭 (HF) 检测对于及时的医疗干预至关重要.
- 现有的高频检测方法需要提高准确性和效率.
- 先进的计算方法可以增强像HF这样的复杂疾病的诊断能力.
研究的目的:
- 引入一种新的混合三阶段专家系统,用于增强心力衰竭检测.
- 通过综合计算方法提高心力衰竭诊断的准确性和效率.
- 为医疗保健专业人员提供强大的决策支持工具,用于识别心力衰竭.
主要方法:
- 使用堆叠自编码器 (AE) 进行特征提取.
- 通过 [公式:参见文本] 处罚的支持矢量机器 (SVM) 进行特征选择.
- 使用具有RBF内核的非线性SVM进行分类.
主要成果:
- 获得了高精度 (97.78%),灵敏度 (97.56%) 和特异性 (97.96%).
- 与当前最先进的方法相比,证明了卓越的性能.
- 使用基准心力衰竭数据集与包括MCC和AUC在内的指标进行验证.
结论:
- 拟议的混合专家系统为心力衰竭检测提供了强大而高效的解决方案.
- 该系统能够在减少功能集的情况下实现高性能,这突显了其效率.
- 这种方法为医疗保健专业人员在诊断心力衰竭方面提供了宝贵的决策支持.
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