Prediction of aortic diameter values in healthy Turkish infants, children, and adolescents by using artificial neural network.
The aorta is the largest vessel in the systemic circuit. Its diameter is very important to guess for child before adult age, due to growing up body. Aortic diameter, one of the cardiac values, changes in time. Evaluation of the cardiac structures and generating a valid regional curve requires a large study group experience for accurate data on normal values. In this study, our aim is to estimate aortic diameter values without curve of charts. Using real sample of the all groups has been predicted using a hybrid system based on combination of Line Based Normalization Method (LBNM) and Artificial Neural Network (ANN) with Levenberg-Marquardt (LM) algorithm. In this study, aortic diameter values dataset divided into two groups as 50% training-50% testing of whole dataset. In order to show the performance of the proposed method, two fold cross validation and prevalent performance measuring methods, Mean Square Error (MSE), Absolute Deviation (AD), Root Mean Square Error (RMSE), statistical relation factor T and R2, have been used. The obtained MSE results from combination of Min-Max normalization and ANN, combination of Decimal Scaling and ANN, combination of Z-score and ANN, and combination of LBNM and ANN (the proposed method) are 0.00517, 0.001299, 0.006196, and 0.000145, respectively. For the suggested method, error's results have been given discretely for every age up to adult age. The results are compared to real aortic diameter values by expert with nine year experiences in medical area. These results have shown that the proposed method can be confidently used in the prediction of aortic diameter values in healthy Turkish infants, children and adolescents.
Akdemir
Bayram
B
Department of Electrical and Electronics Engineering, Selcuk University, 42075 Konya, Turkey. bayakdemir@selcuk.edu.tr
Oran
Bülent
B
Gunes
Salih
S
Karaaslan
Sevim
S
D000293:Adolescent; D000465:Algorithms; D001011:Aorta / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D002648:Child; D002675:Child, Preschool; D005544:Forecasting; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016571:Neural Networks, Computer; D012016:Reference Values; D014421:Turkey; D014463:Ultrasonography