Prediction of Aortic Diameter Values in Healthy Turkish Infants, Children, and Adolescents by Using Artificial Neural Network
Küçük Resim Yok
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 R-2, 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.
Açıklama
Anahtar Kelimeler
Aortic diameter, Echocardiography, Pediatric cardiology, Normalization, Line based, Artificial neural network
Kaynak
JOURNAL OF MEDICAL SYSTEMS
WoS Q Değeri
Q4
Scopus Q Değeri
Q1
Cilt
33
Sayı
5
