世界中医药2025，Vol.20Issue(18)：3337-3345,9.DOI:10.3969/j.issn.1673-7202.2025.18.023
基于机器学习的腰椎间盘突出症中医证候分类模型构建及验证
Development and Validation of Traditional Chinese Medicine Syndrome Classification Models for Lumbar Disc Herniation Based on Machine Learning
摘要
Abstract
Objective:To establish and validate machine learning-based prediction models for the classification of common tradi-tional Chinese medicine(TCM)syndromes of lumbar disc herniation(LDH).Methods:Using an epidemiological survey,data from 606 patients with LDH treated at the Affiliated Hospital of Gansu University of Chinese Medicine were collected.TCM information from the four diagnostic methods was obtained according to a TCM syndrome data collection scale.After data preprocessing and di-mensionality reduction,the dataset was randomly divided into a training set(424 cases)and a test set(182 cases)at a ratio of 7:3.Six machine learning algorithms,including support vector machine(SVM),decision tree(DT),naïve Bayes(NB),random for-est(RF),extreme gradient boosting(XGBoost),and artificial neural network(ANN),were used to construct classification models.Ten-fold cross-validation was applied for parameter optimization.Model performance was evaluated using accuracy,sensitivity,spe-cificity,and the area under the receiver operating characteristic(ROC)curve(AUC).Results:Principal component analysis was applied to reduce the dimensionality of TCM symptom data,yielding eight common factors.Among the LDH TCM syndrome classifi-cation models constructed using the six algorithms,the accuracies of RF,SVM,DT,XGBoost,NB,and ANN were 82.42％,91.21％,87.91％,90.11％,92.86％,and 88.46％,respectively.The corresponding AUC values were 0.942 2,0.984 8,0.947 9,0.895 5,0.909 8,and 0.966 4,respectively.Models constructed using the SVMand NB algorithms achieved both accura-cy and AUC values greater than 0.9.Conclusion:This study successfully developed and validated classification models for common TCM syndromes of LDH.Models constructed using the SVMand NB algorithms demonstrated superior performance compared with other machine learning models and are more suitable for LDH TCM syndrome classification,providing new ideas and methods for re-search on the standardization of TCM syndromes.关键词
腰椎间盘突出症/中医证候/预测模型/机器学习/人工智能算法Key words
Lumbar disc herniation/TCM syndromes/Prediction model/Machine learning/Artificial intelligence algorithm分类
医药卫生引用本文复制引用
王志鹏,张晓刚,张宏伟,赵希云,李元贞,秦大平,任真,郭成龙..基于机器学习的腰椎间盘突出症中医证候分类模型构建及验证[J].世界中医药,2025,20(18):3337-3345,9.基金项目
国家自然科学基金项目(82505367) （82505367）
张晓刚全国名老中医药专家传承工作室建设项目(国中医药人教发〔2022〕75号) （国中医药人教发〔2022〕75号）
甘肃省自然科学基金项目(24JRRA1037) （24JRRA1037）
甘肃省青年人才个人项目(2025QNGR72) （2025QNGR72）
兰州市科学技术局青年科技计划项目(2023-2-47,2023-2-48) （2023-2-47,2023-2-48）
