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2 Biocybernetics and Biomedical Engineering

2 2007

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Evaluation of some statistical methods for referring women for bone densitometry

100%

Enăchescu C.

tom Vol. 27, no. 3

43-58

The aim of our study is to design and compare some predictive models for estimating the Bone Mineral Density score (BMD) t-score. The data were collected, except the t-score, by self-report from 356 women recruited from the Cantacuzino Hospital, Bucharest, Romania, during the period 1998-2003. The following methods are tuned and compared on this data: the General Regression Model (GRM), the Classification Trees (CT) and the Multilayer Perceptrons Network (MLP). Comparatively with a number of different bone densitometry criteria, currently used in clinical practice, we show that each of the above investigated models have a better sensitivity and specificity.

Using multilayer neural networks to estimate the parameters of the nonlinear Baret model

63%

Enăchescu C. , Constantinescu A.

tom Vol. 27, no. 3

95-111

The complexity of the nonlinear models with random parameters doesn't generally allow to resolve in an easy way the parameters estimation problem. In this paper we design and use a multilayer neural network (MLNN) for the parameters estimation. We deal with the Baret model for the temporal evolution of the leaf area index (LAI).