混合加权模糊生产规则提取使用修改的和搜索和BPNNN的BPNNN
Feng Qin1,2, Azlan Mohd Zain1, Kai-Qing Zhou3
1Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310, Malaysia.
Scientific reports
|March 31, 2025
概括
本研究引入了一种新的方法,用于提取加权模糊生产规则 (WFPR),以改进临床决策支持系统 (CDSS). 该方法提高了医疗保健中的诊断准确性和系统可解释性.
科学领域:
- 人工智能在医学中的应用
- 计算智能是一种计算智能.
- 知识工程知识工程知识工程
背景情况:
- 重量模糊生产规则 (WFPR) 对临床决策支持系统 (CDSS) 至关重要,提高了诊断准确性.
- 知识工程桥梁数据驱动的见解和临床决策,但WFPR提取需要优化.
- 现有的方法在高维空间中面临挑战,并避免局部最佳.
研究的目的:
- 提出使用动态维度调整和搜索 (DDA-HS) 和逆向传播神经网络 (BPNNs) 进行增强WFPR提取的综合方法.
- 在CDSS中提高WFPR的准确性和可解释性.
- 为可靠和透明的医疗保健决策支持提供一个强大的框架.
主要方法:
- 开发了动态维度调整和搜索 (DDA-HS) 算法,以优化BPNNs中的初始权重.
- 在DDA-HS中使用吸收马尔科夫链来提高过渡概率和探索.
- 集成的DDA-HS与BPNNs用于增强WFPR提取和分类.
主要成果:
- DDA-HS在PIMA数据集上实现了BPNN分类的74.48%准确度和WFPR分类的77.08%准确度.
- 与现有的优化方法相比,表现出3.6%和6.5%的显著改进.
- 通过WFPR提取展示了BPNNs的增强解释性,揭示了特征影响.
结论:
- 拟议的DDA-HS和BPNN集成为CDSS中WFPR提取提供了一种优越的方法.
- 该方法提高了临床决策过程的准确性和透明度.
- 该框架支持开发更可靠和可解释的医疗保健CDSS.
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