Title :
Efficient Estimation of Osteoporosis using Artificial Neural Networks
Author :
Lemineur, Gerald ; Harba, Rachid ; Kilic, Niyazi ; Ucan, Osman N. ; Osman, Onur ; Benhamou, Laurent
Author_Institution :
Univ. of Orleans, Orleans
Abstract :
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 (BMD) (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.
Keywords :
bone; densitometry; neural nets; artificial neural networks; bone densitometry parameters; classical discriminant analysis; fractal parameters; osteoporosis; Artificial neural networks; Bones; Communication system control; Fractals; Neck; Neurons; Optimization methods; Osteoporosis; Statistical analysis; Testing;
Conference_Titel :
Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE
Conference_Location :
Taipei
Print_ISBN :
1-4244-0783-4
DOI :
10.1109/IECON.2007.4460070