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《Beijing Biomedical Engineering》 2008-03
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A comparison study of data mining algorithms in CHD clinical application

CHEN Jianxin1,2 XI Guangcheng1,WANG Wei2,ZHAO Huihui2,CHEN Jing1 1 Key Laboratory of Complex Systems and Intelligence ScienceInstitute of Automation,Chinese Academy of Sciences,Beijing 100080;2 Beijing University of Chinese Medicine,Beijing 100029  
In China,more and more people take traditional chinese medicine(TCM) to heal coronary heart disease(CHD).Syndrome is the core of TCM theory.Predicting the kind of syndrome for a CHD patient is an important and challenge work.In this paper,based on 1069 cases of clinical epidemiology data of CHD,we employ four kinds of data mining methods and a statistical method as prediction model to learn and classify the data.We use the method of 10fold cross validation to measure the three performance measures,the sensitivity,specificity and accuracy of each algorithm and also record the computation time of each algorithm.We select the best algorithm from each category and put all the algorithms to compare with each other.The results show that Bayesian network is the best in the Bayes methods,with 90.17% sensitivity,88.89%specificity,89.24% accuracy and its computation time is 0.53s.RBFN is the best in the neural network counterpart,sensitivity is 84.07%,specificity 94.23%,accuracy 91.49%,computation time is 1.03s.while Libsvm is the best in SVM category,sensitivity 91.19%,specificity 93.15%,accuracy 92.61%,computation time 1.0s.ADTree is the best in the case of decision tree with a sensitivity of 83.73%,specificity of 95.99% and accuracy 92.61%,computation time 2.78s.Logistic regression's sensitivity is 88.14%,specificity 94.44%,accuracy 92.70% and computation time is 1.09s.Synthesizing all performance measures,SVM is the best,logistic regression is the second,the following is decision tree and Bayes,neural network comes worst.The comparison study here paves a mathematic and physical basis for predicting syndrome in CHD.
【Fund】: 国家973项目(2003CB517105 2003CB517106)资助
【CateGory Index】: R541.4
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