A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning.

IF 1.4 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Mei Zhao, Hengyu Zhou, Jing Wang, Yongyue Liu, Xiaoqing Zhang
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引用次数: 0

Abstract

Background: The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bias. Efficient and accurate TCM constitution identification can play an important role in individualised medicine and healthcare.

Objective: Building an efficient model for identifying traditional Chinese medicine constitutions using objective tongue features and machine learning techniques.

Methods: The DS01-A device was applied to collect tongue images and extract features. We trained and evaluated five machine learning models: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), and CatBoost (CB). Among these, we selected the model with the best performance as the base classifier for constructing our heterogeneous ensemble learning model. Using various performance metrics, including classification accuracy, precision, recall, F1 score, and area under curve (AUC), to comprehensively evaluate model performance.

Results: A total of 1149 tongue images were obtained and 45 features were extracted, forming dataset 1. RF, LGBM, and CB were selected as the base learners for the RLC-Stacking. On dataset 1, RLC-Stacking1 achieved an accuracy of 0.8122, outperforming individual classifiers. After feature selection, the classification accuracy of RLC-Stacking2 improved to 0.8287, an improvement of 0.00165 compared to RLC-Stacking1. RLC-Stacking2 achieved an accuracy exceeding 0.85 for identifying each TCM constitution type, indicating excellent identification performance.

Conclusion: The study provides a reliable method for the accurate and rapid identification of TCM constitutions and can assist clinicians in tailoring individualized medical treatments based on personal constitution types and guide daily health care. The information extracted from tongue images serves as an effective marker for objective TCM constitution identification.

基于舌头特征的机器学习中药体质辨识新方法。
背景:中医体质理论有助于优化个性化保健方案。然而,目前中医体质辨识主要依靠主观偏差的低效问卷。高效、准确的中医体质辨识可在个体化医疗保健中发挥重要作用:利用客观舌象特征和机器学习技术建立高效的中医体质辨识模型:方法:使用 DS01-A 设备采集舌头图像并提取特征。我们训练并评估了五个机器学习模型:支持向量机 (SVM)、决策树 (DT)、随机森林 (RF)、LightGBM (LGBM) 和 CatBoost (CB)。在这些模型中,我们选择了性能最好的模型作为构建异构集合学习模型的基础分类器。使用各种性能指标,包括分类准确率、精确度、召回率、F1 分数和曲线下面积(AUC),来综合评估模型的性能:共获得 1149 张舌头图像,提取 45 个特征,形成数据集 1。RF、LGBM 和 CB 被选为 RLC-Stacking 的基础学习器。在数据集 1 上,RLC-Stacking1 的准确率达到了 0.8122,优于单个分类器。经过特征选择后,RLC-Stacking2 的分类准确率提高到 0.8287,比 RLC-Stacking1 提高了 0.00165。RLC-Stacking2 对每种中医体质类型的识别准确率都超过了 0.85,显示了出色的识别性能:结论:该研究为准确、快速识别中医体质提供了可靠的方法,可帮助临床医生根据个人体质类型进行个体化治疗,并指导日常保健。从舌苔图像中提取的信息可作为客观中医体质辨识的有效标记。
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来源期刊
Technology and Health Care
Technology and Health Care HEALTH CARE SCIENCES & SERVICES-ENGINEERING, BIOMEDICAL
CiteScore
2.10
自引率
6.20%
发文量
282
审稿时长
>12 weeks
期刊介绍: Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered: 1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables. 2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words. Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics. 4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors. 5.Letters to the Editors: Discussions or short statements (not indexed).
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