Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

作     者：Liang Ding Xin-you Zhang Di-yao Wu Meng-ling Liua Liang Ding;Xin-you Zhang;Di-yao Wu;Meng-ling Liu

作者机构：School of ComputerJiangxi University of Traditional Chinese MedicineNanchang 330004Jiangxi ProvinceChina School of PharmacyJiangxi University of Traditional Chinese MedicineNanchang 330004Jiangxi ProvinceChina 

出 版 物：《Journal of Integrative Medicine》 (结合医学学报（英文版）)

年 卷 期：2021年第19卷第5期

页      面：395-407页

核心收录：

学科分类：1007[医学-药学(可授医学、理学学位)] 12[管理学] 1005[医学-中医学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 100214[医学-肿瘤学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：financially supported by the National Natural Science Foundation (No. 81660727) 

主　　题：Primary liver cancer Syndrome type Particle swarm Extreme learning machine Fuzzy mathematics 

摘      要：Objective: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer(PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine(TCM) ***: From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining10,060 electronic medical records, which were randomly divided into a training set and a test *** on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification ***: The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%,respectively. The classification accuracy rates of the models for all syndromes in this paper were between82.15% and 93.82%.C