Title :
Research on diagnostic models of pneumonia syndromes in TCM based on fuzzy-neural net
Author :
Jiansheng, Li ; Jinliang, Hu ; Jianjing, Shen ; Zhiwan, Wang ; Suyun, Li ; Jiehua, Wang
Author_Institution :
Inst. of Gerontology, Henan Coll. of TCM, Zhengzhou, China
Abstract :
To explore methods of establishing standard models of pneumonia syndromes in TCM (traditional Chinese medicine) by studying the results of data mining of pneumonia. First, in accordance with the selection criterion, 1058 pieces of clinical data from the patients with pneumonia were collected by clinical epidemiological methods. Secondly, fuzzy neural net models were built up on the basis of dynamic kohonen network and their reliability was tested with the Fisher-iris data. Then, with the help of the models the clinical data was studied and the diagnostic criterion for commonly-seen syndromes of pneumonia was obtained according to TCM basic theories. Simultaneously, the reliability was tested by data check-up. The coincident diagnostic rate reached 86% in comparison of the diagnostic criterion and original diagnostic data. The model, for its rational characteristics, can be applied to the study of diagnostic criterion for pneumonia syndromes.
Keywords :
data mining; diseases; fuzzy neural nets; medical diagnostic computing; patient diagnosis; Fisher-iris data; TCM; clinical data; clinical epidemiological method; coincident diagnostic rate; data check-up; data mining; diagnostic criterion; diagnostic model; dynamic kohonen network; fuzzy-neural net; pneumonia syndrome; reliability; selection criterion; traditional Chinese medicine; Data mining; Educational institutions; Electronic mail; Fuzzy neural networks; Fuzzy sets; Gerontology; Lungs; Medical diagnostic imaging; Neural networks; Testing; data mining; diagnostic criterion for syndromes; dynamic kohonen network; pneumonia;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5191911
