广州中医药大学学报2026，Vol.43Issue(1)：246-256,11.DOI:10.13359/j.cnki.gzxbtcm.2026.01.034
基于集成学习的IgA肾病中医辨证智能模型的构建及应用
Construction and Application of Integrated Learning-Based TCM Syndrome Differentiation AI Model for IgA Nephropathy
摘要
Abstract
Objective To construct an traditional Chinese medicine(TCM)syndrome differentiation artificial intelliaence(AI)model for IgA nephropathy using machine learning methods.Methods The information of medical records of IgA nephropathy was retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data Knowledge Service Platform,VIP Chinese Science and Technology Journal Database,and China Biological Medicine Database(CBM)from the establishment of each database to September 1,2024,and from the Ancient and Modern Medical Case Cloud Platform.A TCM information database for IgA nephropathy was established,and the data samples were split into a training set and a test set in a ratio of 7∶3.Machine learning algorithms(base learner)such as the tuned support vector machine(SVM),gradient boosting machine(GBM),random forest(RF),and multilayer perceptron(MLP)were applied for the quantitative analysis of the dataset,and then an TCM syndrome differentiation AI model for IgA nephropathy was constructed.An ensemble algorithm was then applied to integrate the four classifier models,forming a strong learner.The model performance was evaluated using five-fold cross-validation,with evaluation metrics of accuracy,precision,recall,F1-score,and the area under the receiver operating characteristic curve(AUC).Results The input variables comprised 63 items of the TCM four-examination information of IgA nephropathy,including 38 symptoms,13 kinds of tongue manifestations,and 12 kinds of pulse manifestations.The output variables were four TCM syndrome types,namely externally contracted wind-heat syndrome,lung-spleen qi deficiency syndrome,qi-yin deficiency syndrome,and qi-yin deficiency complicated with damp-heat syndrome.The fitting performance of each model is satisfactory,with accuracy,precision,recall,F1-score,and AUC values all exceeding 0.83.The ensemble model showed the highest scores in accuracy,precision,and recall,and the values of its accuracy,AUC,F1-score,recall,and precision were 0.92,0.98,0.85,0.88,and 0.85 respectively,being higher than those of other models.The results indicated that the performance of ensemble model was superior to that of the other models.The interpretation of parameters in TCM corresponded well with TCM examination principles.Conclusion The constructed ensemble model integrates the advantages of individual classifiers,exhibiting enhanced stability,performance,and generalization capability compared to single base learner over individual models.This study demonstrates the methodological feasibility of applying AI to the development of TCM syndrome differentiation AI models for IgA nephropathy.关键词
IgA肾病/中医辨证模型/医案/人工智能/机器学习/集成学习/方法学Key words
IgA nephropathy/traditional Chinese medicine(TCM)syndrome differentiation model/medical records/artificial intelligence(AI)/machine learning/ensemble learning/methodology分类
医药卫生引用本文复制引用
吴倩,陈意,余浩凯,周奇,邹川..基于集成学习的IgA肾病中医辨证智能模型的构建及应用[J].广州中医药大学学报,2026,43(1):246-256,11.基金项目
国家自然科学基金面上项目(编号:81873142) （编号:81873142）
国家中医药管理局黄春林全国名老中医传承工作室项目(编号:2012KT1301) （编号:2012KT1301）
