Comparative study of linear mixed-effects and artificial neural network models for longitudinal unbalanced growth data of Madras Red sheep

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.