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Open Access
Copyright: The authors. This article is an open access
article licensed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0) which permits unrestricted use,
distribution and reproduction in any medium, provided the work is properly
cited.
Research
(Published
online: 06-02-2014)
2. Comparative study of linear mixed-effects and
artificial neural network models for longitudinal unbalanced growth data of
Madras Red sheep - R. Ganesan, P. Dhanavanthan, C. Kiruthika, P.
Kumarasamy and D. Balasubramanyam
Veterinary World, 7(2): 52-58
doi:
10.14202/vetworld.2014.52-58
Abstract
Aim: The present study was
conducted to compare the predictive ability of artificial neural
network (ANN) models developed using multilayer perceptron (MLP)
and radial basis function (RBF) architectures with linear
mixed-effects model for the longitudinal growth data of Madras red
sheep.
Materials and Methods: Repeated monthly body weight
measurements from birth to 24 months of age of 1424 sheep were
used for the analysis. Linear mixed-effects model was developed by
progressively fitting unconditional linear growth, unconditional
quadratic growth, conditional quadratic growth model and
conditional quadratic growth models accommodating different error
variance- covariance structures. The time invariant covariates
such as gender of lamb, season of birth and dam's weight at
lambing were also used for the analysis. The best model was
identified using Akaike Information Criterion. Subsequently, ANN
models using MLP and RBF architectures were developed for the same
data and the predictive ability of the two modeling procedures
were compared using different evaluation criteria.
Results: Conditional quadratic model with heterogeneous
Autoregressive of order 1 (AR(1)) covariance structure fitted
using mixed model approach was found to be good with covariates,
gender of lamb and dam's weight at lambing showing marked
influence on all the growth parameters. Season of birth was found
to be significant only for growth rate and not for the average
birth weight. Between the two ANN architectures, MLP performed
better than RBF and also ANN model based on MLP architecture was
better than the best linear mixed model identified in this study.
Conclusion: In this study, the potential of ANN as an
alternative modeling technique was evaluated for the purpose of
predicting unbalanced longitudinal growth data of Madras Red
sheep. As the predictive ability of the ANN model with MLP
architecture yielded better results, ANN models can be considered
as an alternative tool by animal breeders to model longitudinal
animal growth data.
Keywords: artificial neural network, growth curves, linear
mixed-effects models, longitudinal data, sheep.
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