Mining Data from Hemodynamic Simulations for Human Aorta

被引：0

作者：

Tosovic, Milena ^{[1
,2
]}

Radovic, Milos ^{[5
]}

Petrovic, Dejan ^{[5
]}

Milasinovic, danko ^{[5
]}

Devedzic, Goran ^{[4
]}

Rankovic, Vesna ^{[4
]}

Filipovic, Nenad ^{[2
,3
]}

机构：

[1] ComTrade ITSS, Kragujevac, Serbia

[2] Fac Mech Engn, Kragujevac, Serbia

[3] Res & Dev Ctr Bioengn BioIRC, Kragujevac, Serbia

[4] Fac Mech Engn, Kragujevac, Serbia

[5] Res & Dev Ctr Bioengn BioIRC, Kragujevac, Serbia

来源：

IPSI BGD TRANSACTIONS ON INTERNET RESEARCH | 2011年 / 7卷 / 02期

关键词：

k-nearest neighbor (k-NN); multilayer perceptron neural network (MLPNN); backpropagation algorithm; CFD simulation;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To analyze the influence of geometric parameters and flow on maximal wall shear stress (MWSS), maximum pressure and total force in the human aorta, the computer simulations were run to generate the data pertaining to this phenomenon. These data were used for training and testing two data mining models: multilayer perceptron neural network trained with backpropagation algorithm and k-nearest neighbor algorithm. The results showed that both models have strong ability in predicting target variables.

引用

页码：6 / 10

页数：5

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