一种基于舌头特征的新方法,用于识别传统中医药构成,使用机器学习
概括
这项研究开发了一种高效的机器学习模型,使用舌头图像进行传统中医 (TCM) 宪法识别. 该模型准确地识别了个别的TCM宪法,帮助个性化医疗保健和日常健康指导.
科学领域:
- 综合医学是一个整体的医学.
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 传统中医 (TCM) 宪法理论对于个性化医疗保健至关重要.
- 目前的TCM构成识别依赖于主观和低效的问卷.
- 需要客观和准确的识别方法来实现个性化医疗.
研究的目的:
- 开发一个高效的机器学习模型用于TCM构成识别.
- 利用客观的舌头特征进行准确的诊断.
- 改进现有的主观识别方法.
主要方法:
- 使用DS01-A设备收集了舌头图像,并提取了特征.
- 训练和评估了五种机器学习模型 (SVM,DT,RF,LGBM,CB).
- 构建了一个异质集体学习模型 (RLC-Stacking),并通过特征选择进行了优化.
主要成果:
- RLC-Stacking组合模型在TCM结构识别中实现了高精度.
- 在特征选择后,RLC-Stacking2达到0.8287.7的分类准确度.
- 该模型表现出卓越的性能,每个TCM构造类型的精度超过0.85.
结论:
- 开发的方法提供了可靠,准确和快速的TCM构成识别.
- 舌头图像分析为TCM体质评估提供了有效的客观标记.
- 这种方法可以帮助临床医生量身定制医疗治疗,指导医疗保健.
相关概念视频
00:49The Tongue and Taste Buds
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01:26Assessment of the Mouth
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Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.