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基于多模型融合的传统中医宪法的智能识别研究

Yue Yao¹, Cai Xiaohong^1,2

¹Department of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, China.

Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan

|February 25, 2026

概括

本研究介绍了一种使用多模型融合算法的自动化系统,用于对传统中医 (TCM) 构成类型进行分类,简化诊断并支持个性化健康管理.

科学领域:

传统中医学的计算智能 (TCM).
开发用于个性化医疗的自动诊断系统.

背景情况:

传统中医 (TCM) 宪法打字是复杂和主观的.
自动化系统可以提高TCM诊断的客观性和效率.

研究的目的:

开发一个用于识别和分类TCM构成类型的自动化系统.
利用多模型融合算法来提高分类准确性.

主要方法:

设计了一个缩小的物理信息收集表格,以便有效地获取数据.
应用了多模型融合方法,整合了各种机器学习算法 (SVM,天真贝叶斯,决策树,随机森林,物流回归,MLP,KNN,梯度增强,自适应集体学习,RNN).
利用软投票策略,将模型预测结合起来,以获得最佳的性能.

主要成果:

多模型融合显著改善了大多数TCM构造类型的分类性能.
高精度,精度,回忆和F1分数在缺陷,缺陷和停滞方面取得了很高的准确性.
对阴缺乏症的分类性能需要进一步优化.

结论:

引入了一种新的自动化方法,用于使用多模型融合进行TCM结构分类.
该系统简化了构成识别,为智能TCM诊断提供了一个框架.
结果支持个性化的健康管理和TCM的临床决策.

关键词:

传统中国医学的宪法.算法算法是一种算法.多模型的融合融合.投票分类器的投票分类器

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