Name
Title | ||
|---|---|---|
TCM syndromes diagnostic model of hypertension: Study based on Tree Augmented Naive Bayes |
Abstract | ||
|---|---|---|
To establish a Bayesian diagnosis model of TCM symptoms by using the hypertension epidemiological Syndrome databases. The Clementine 12.0 software is used to build the Tree Augmented Naive Bayes models, calculate the Bayesian conditional probability, and compare the forecast accuracy of the syndrome diagnosis model. The training sample had 384 cases and calculated 69 symptoms and signs, without prior knowledge, the prediction accuracy rate of the training model are 72.11%, and with the prior knowledge, the testing sample had 384 cases, the prediction accuracy rate of testing model is up to 78.55%. Through the sample study, Bayesian networks can improve the prediction accuracy; we can build a more accurate hypertension diagnosis model through the current work. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BIBMW.2011.6112481 | BIBM Workshops |
Keywords | Field | DocType |
hypertension epidemiological syndrome database,prediction accuracy,traditional chinese medicine,medical disorders,tree augmented naive bayes,prediction rate,accurate hypertension diagnosis model,bayes methods,bayesian diagnosis model,bayesian conditional probability,syndrome diagnosis model,syndrome diagnosis model forecast accuracy,prior knowledge,clementine 12.0 software,forecast accuracy,bayesian network,training model,data mining,prediction accuracy rate,tan bayes model,medical computing,tree augmented naive bayes model,tcm syndrome diagnostic model,diagnostic model,tcm syndrome diagnosis model,patient diagnosis,conditional probability | Data mining,Naive Bayes classifier,Conditional probability,Computer science,Software,Bayesian network,Artificial intelligence,Machine learning,Bayesian probability | Conference |
ISSN | ISBN | Citations |
2163-6966 | 978-1-4577-1612-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen-wei Ouyang | 1 | 0 | 0.34 |
| Xiao-Zhong Lin | 2 | 0 | 1.01 |
| Yi Ren | 3 | 0 | 0.34 |
| Yi Luo | 4 | 0 | 1.35 |
| Yuntao Liu | 5 | 0 | 1.01 |
| Jiamin Yuan | 6 | 0 | 1.01 |
| Ai-hua Ou | 7 | 1 | 3.86 |
| Guo-Zheng Li | 8 | 368 | 42.62 |