NEURAL NETWORK PREDICTIVE MODELS TO DETERMINE THE EFFECT OF BLOOD COMPOSITION ON THE PATIENT-SPECIFIC ANEURYSM

被引:0
作者
Quadros, Jaimon Dennis [1 ]
Pahlavani, Hamed [2 ]
Ozdemir, I. Bedii [2 ]
Mogul, Yakub Iqbal [3 ]
机构
[1] Univ Bolton, Fac Mech Engn, RAK Acad Ctr, Ras Al Khaymah 16038, U Arab Emirates
[2] Istanbul Tech Univ, Fac Mech Engn, Fluids Grp, TR-34437 Istanbul, Turkiye
[3] Univ Bolton, Natl Ctr Motorsport Engn, Deane Rd, Bolton BL3 5AB, England
关键词
Back propagation neural network; aneurysm; effect of blood composition; predictive models; WEAR BEHAVIOR; RISK;
D O I
10.1142/S0219519423500768
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Using the data obtained from the computational fluid dynamics simulations, a back-propagation neural network model was developed to predict the velocity magnitudes and the instantaneous wall shear stresses in two patient-specific aneurysms. The models were also used to determine the effect of the blood composition on the rapture risk of the aneurysms. Based on the possible combination, five back propagation models were developed. The architecture of five models is determined based on number of neurons in the hidden layer. All the models in each algorithm were trained and tested. The accuracy of the developed models was evaluated through statistical analysis of the network output in terms of mean absolute error, root mean squared error, mean squared error, and error deviation. According to the results obtained, all BPA effectively predicted velocity magnitude and instantaneous wall shear stress. Model 1 was, however, less accurate when compared to the other five models, as it had one neuron in its hidden layer. The analysis confirms that the neuron number in the hidden layer play a definitive role in predicting the respective outputs. The performance assessment all of the back-propagation models revealed that the error incurred was acceptable. The algorithms' training and testing in this study were satisfactory, since the network output was in reasonably good conformity with the target computational fluid dynamics result.
引用
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页数:24
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