title: In silico syndrome prediction for coronary artery disease in traditional chinese medicine.
abstract: Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM.
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Lu
authors.forename: Jianxin
authors.initials: J
authors.lastname: Chen
authors.forename: Huihui
authors.initials: H
authors.lastname: Zhao
authors.forename: Yibo
authors.initials: Y
authors.lastname: Gao
authors.forename: Liangtao
authors.initials: L
authors.lastname: Luo
authors.forename: Xiaohan
authors.initials: X
authors.lastname: Zuo
authors.forename: Qi
authors.initials: Q
authors.lastname: Shi
authors.forename: Yiping
authors.initials: Y
authors.lastname: Yang
authors.forename: Jianqiang
authors.initials: J
authors.lastname: Yi
authors.forename: Wei
authors.initials: W
authors.lastname: Wang