Title :
A Computerized Diagnostic Model Based on Naive Bayesian Classifier in Traditional Chinese Medicine
Author_Institution :
Coll. of Comput. Sci. & Inf. Eng., Zhejiang Gongshang Univ., Hangzhou
Abstract :
Traditional Chinese Medicine (TCM) is one of the most important complementary and alternative medicines. In this paper, a novel computerized diagnostic model is proposed for promoting standardization and popularization of TCM diagnosis. Firstly, the symptoms are selected by learning Bayesian network structure from a database of cases incorporating with mutual information theory. The Markov blanket of the target variable in the structure is selected as symptom set. Secondly, the mapping relationships between the symptom set and diagnostic results are constructed based on naive Bayesian classifier. The model is used to make the quantitative diagnosis of apoplexy and obtains relative reliable predictions. The rate of predictive accuracy in diagnosing apoplexy reaches 96.7%. The results suggest that the model constructed is feasible and effective and can be expected to be useful in the modernization of TCM.
Keywords :
Bayes methods; belief networks; medical diagnostic computing; patient diagnosis; pattern classification; Markov blanket; TCM diagnosis; apoplexy; computerized diagnostic model; learning Bayesian network structure; mutual information theory; naive Bayesian classifier; traditional Chinese medicine; Accuracy; Bayesian methods; Biomedical engineering; Databases; Diseases; Medical diagnostic imaging; Mutual information; Niobium compounds; Predictive models; Uncertainty; TCM; quantitative diagnosis;
Conference_Titel :
BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-0-7695-3118-2
DOI :
10.1109/BMEI.2008.292
