Abstract
In this (modest) study, we developed artificial neural network (ANN) models for predicting body weight using various independent (input) variables in eight-week old New Zealand white purebred and crossbred rabbits. From the whole data sets of similar age groups, 75 percent were used to train the neural network model and 25 percent were used to test the effectiveness of the model. Five predictor variables were used viz, breed, sex, heart girth, body length and height at wither as input variables and body weight was considered as dependent variable from the model. The ANN used was multilayer feed forward network with back propagation of error for efficient learning. Our ANN models (with R2 = 0.68 at ten thousand iterations, and R2 = 0.71 one million iterations) performed better than traditional multivariate linear regression (MLR) models (R2 = 0.66) indicating that the ANN models were able to more accurately capture how the variations in input variables explained the variations in body weight. It is concluded that ANN models are more powerful than MLR models in predicting animals’ body weight. Nonetheless, we recognize that fitting an ANN model requires more computation resources than fitting a tradition MLR model but the benefits of its accuracy outweigh any demerit from the associated computation overhead.
Highlights
Traditional statistical prediction and classification methods have a number of limitations and results from them are often not the best possible
artificial neural network (ANN) provide a single tool for solving many problems in which traditionally statistical methods can or cannot provide acceptable solutions for
This limited use of ANN in animal science is paradoxical as data analyses are often done in this field despite that a few studies [3], etc. have shown ANNs to be more powerful than most traditional statistical prediction methods
Summary
Traditional statistical prediction and classification methods (such as linear regression, logistic regression, principal component analysis, discriminant analysis, k-nearest neighbor classification, etc.) have a number of limitations (such as the assumptions upon which they are based [1]) and results from them are often not the best possible. The number of the use of ANNs is still few in animal science (and a number of other fields). ANNs attempt to solve problems through explicit learning [4] [5]. This often makes it more computationally intensive. Its strengths transcend this mild limitation and we were excited by the results we obtained from using ANN models in this study
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