基于遗传算法与KNN融合的中医体质量表简化研究  

Simplified Study of Constitution in Chinese Medicine Questionnaire Based on Genetic Algorithm and KNN Method

作　　者：管树桃 李红岩[1] 郎许锋 李灿 周作建 胡孔法[1,2] 战丽彬 Guan Shutao;Li Hongyan;Lang Xufeng;Li Can;Zhou Zuojian;Hu Kongfa;Zhan Libin(School of Artificial Intelligence and Information Technology,Nanjing University of Chinese Medicine,Nanjing 210023,China;Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor,Nanjing 210023,China;Center for Innovative Engineering Technology in Traditional Chinese Medicine,Liaoning University of Traditional Chinese Medicine,Shenyang 110847,China)

机构地区：[1]南京中医药大学人工智能与信息技术学院,南京210023 [2]江苏省中医药防治肿瘤协同创新中心,南京210023 [3]辽宁中医药大学中医药创新工程技术中心,沈阳110847

出　　处：《世界科学技术-中医药现代化》2023年第10期3364-3369,共6页Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology

基　　金：国家科学技术部重点研发计划中医药现代化研究专项(2018YFC1704400):阴虚证辨证标准的系统研究,负责人:周作建;国家自然科学基金委员会面上项目(82074580):基于知识图谱的现代名老中医诊治肺癌用药规律及其机制研究,负责人:胡孔法

摘　　要：目的针对中医体质量表在评估个人体质时条目多和填写时间久的问题,研究利用人工智能技术进行属性选择,帮助构建中医体质量表简短版本。方法分析由江苏省中医院体检科提供的中医体质数据,其中有特定的目标变量作为体质类型的分类。采用遗传算法的特征选择、交叉验证和KNN分类算法作为过滤器筛选问题,并通过问题子集规模、KNN分类准确率和填写时间评估效果。结果该方法选择出具有31个问题的中医体质量表简短版本,且在模型中的分类平均准确率为86.16%,时间提快了48.5%。结论该算法可以有效地找出较好的问题子集,实现降维并有一定的准确性,从而帮助简化中医体质量表。Objective Aiming at the problems of many items and long time to fill in the Constitution in Chinese Medicine Questionnaire(CCMQ)when evaluating individual constitution,the research uses artificial intelligence technology to select attributes,and to help construct a short version of the CCMQ.Methods Analyzing the constitution data provided by the Physical Examination Department of Jiangsu Province Hospital of Traditional Chinese Medicine,there are specific target variables as the classification of constitution types.Feature selection of genetic algorithm,crossvalidation and KNN classification algorithm are used as filters to select problems,and the effect is evaluated by problem subset size,KNN classification accuracy and filling time.Results The method selected a short version of the CCMQ with 31 problems,and the average classification accuracy in the model was 86.16%,and the time was improved by 47.7%.Conclusion The algorithm can effectively find a better problem subset,achieve dimensionality reduction and have certain accuracy,thus helping to simplify the CCMQ.

关 键 词：中医体质 中医体质量表 遗传算法 KNN算法 

分 类 号：R-058[医药卫生]