Paper Info
Title
Multi-Label Symptom Analysis And Modeling Of Tcm Diagnosis Of Hypertension
Abstract
Traditional Chinese Medicine (TCM) has been used for diagnosis of hypertension and has significant advantages. Symptom analysis and modeling of TCM provides a way for the clinician to produce a service to users to accurately and efficiently diagnose hypertension. In this study, an ensemble learning framework based on network clustering analysis with information fusion is proposed. We first analyze the frequency distribution and cluster heat map of TCM hypertension clinical cases, and establish a network based on the syndrome and symptom of cases. Through the analysis of community networks, we get the dominant and subordinate syndrome and construct a sub-classifier to co-train and improve the performance of the classifier. Then we use MLKNN and RAkEL-SVM multi-label classifiers to train and test the cases. Considering the result of 10-fold cross validation, we discover that ML-KNN and RAkEL-SVNI with information fusion have better performance than traditional learning methods without information fusion. For all evaluation criteria, the average precision of ML-KNN is higher, and the F-Measure does not vary substantially. But the averaged recall of RAkEL-SVM is significantly higher.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621173
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
Field
DocType
network clustering analysis, ML-KNN, RAkEL SVM, information Usion, Traditional Chinese Medicine
Network clustering,Computer science,Artificial intelligence,Classifier (linguistics),Ensemble learning,Cross-validation,Information fusion,Recall,Machine learning
Conference
ISSN
Citations 
PageRank 
2156-1125
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Heng Weng1133.02
Ziqing Liu241.44
Andrew S. Maxwell392.30
Xiantao Li400.34
Chaoyang Zhang523022.23
Enwei Peng600.34
l i guozheng7262.54
Aihua Ou861.51