Efficient estimation of osteoporosis using Artificial Neural Networks
Küçük Resim Yok
Tarih
2007
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this communication, Artificial Neural Network (ANN) is applied to discriminate osteoporotic fracture and control cases in a group of 304 patients. ANN is one of the popular methods in optimization of complex engineering problems compared to the classical statistical methods. In our study group, we consider some parameters as inputs: three bone densitometry parameters (HMD) (Femoral neck BMD, Total Body BMD and L2L4 spine BMD), three fractal parameters [1,5] (Hmin. Hmean, Hmax), and age of the patient. We studied three ANN structures with various inputs and hidden neurons. We have reached up to 81.66% correct classification. In comparison we have tested a classical discriminant analysis (Mahalanobis-Fisher) and we only obtained 72% of correct classification. We can conclude that ANN is one of the promising methods in the diagnosis of osteoporosis. ©2007 IEEE.
Açıklama
33rd Annual Conference of the IEEE Industrial Electronics Society, IECON -- 5 November 2007 through 8 November 2007 -- Taipei -- 73038
Anahtar Kelimeler
Kaynak
IECON Proceedings (Industrial Electronics Conference)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
