In silico syndrome prediction for coronary artery disease in traditional chinese medicine.

Evidence-based Complementary and Alternative Medicine : ECAM
Peng LuWei Wang

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 CA...Continue Reading

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Citations

May 11, 2013·Evidence-based Complementary and Alternative Medicine : ECAM·Wen DaiYunling Zhang
Jul 10, 2021·Biomedicine & Pharmacotherapy = Biomédecine & Pharmacothérapie·Tingting DiChuanjian Lu

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Software Mentioned

SMOSVM
MBEGA
MBFS

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