Prediction of stress responses in goats: comparison of artificial neural network and multiple regression models

Abstract

This study was conducted to determine if artificial neural networks (ANN) can be used to more accurately predict physiological stress responses in goats compared with statistical regression. Prediction models were developed for plasma cortisol and glucose concentrations, creatine kinase (CK) activity, neutrophil (N) and lymphocyte (L) counts, and N:L ratio as a function of time (0, 1, 2, 3, and 4 h; n = 16 goats per time) after a 2.5 h transportation (input 1) and stocking density (25 vs. 50 goats; input 2). However, input 2 was not included in the final models because density did not have a significant effect. The NeuralWorks Predict® software and SAS were used to develop ANN and regression models, respectively. Backpropagation (BP) and Kalman filter (KF) learning rules were used to develop nonparametric models. Correlations between predicted and observed values were better with ANN-BP (R values = 0.87, 0.67, 0.56, 0.27, 0.42, and 0.53) and ANN-KF (R values = 0.84, 0.67, 0.58, 0.27, 0.42, and 0.50) models for cortisol, glucose, CK, N, L, and N:L ratio, respectively, than with regression models (R values =0.85, 0.52, 0.27, 0.13, 0.31, and 0.12). The results showed that the ANN models can predict responses more robustly compared with statistical regression.

Inactivation ofEscherichia coliK12 by pulsed UV light on goat meat and beef: microbial responses and modelling

“…Use of more than one statistical index is recommended to evaluate precision, bias and accuracy to assess prediction efficiency of models due to discrepancy in the method of calculation of these statistical indices. This is particularly essential for biological data with practical applications, which are often difficult to predict due to their inherent complexities (Kannan et al ., 2020).…”

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

“…Cascade learning, also known as cascade–correlation, is a supervised learning algorithm, which begins with a nominal network and then trains and adds hidden units one at a time, and always connecting all the previous units to the current unit [ 38 ]. The “cascade” part refers to the stepwise mode of construction of the structure, and the “correlation” part refers to the way in which the hidden units are trained by maximizing the correlation between output of hidden units and the desired output of the network across the training data [ 41 ].

Figure 1 Schematic representation of multilayer perceptron neural network model.

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“…The network configurations for models were approached empirically, and the model that performed well with validation dataset was selected. To evaluate the performance of ANN models, several statistical indices such as Pearson correlation (R), mean relative percentage residual (MRPR, %), bias factor, mean absolute relative residual (MARR, %), accuracy factor, and standard deviation were calculated [ 41 ]. Since R values measure the strength and direction of a linear relationship between predicted and observed outputs, they were used to select the best models (highest R values were considered as best) [ 26 ].…”

Study on IC50 prediction of targeted drugs for hepatoma cells based on SSA-BP neural network

“…Back Propagation (BP) neural network has become the most mature prediction algorithm with complete theoretical system because of its high fault tolerance, fast and stable, simple implementation process and so on [4]. Kannan et al [5] applied BP neural network to verify the accuracy and reliability of goat physiological stress response prediction. However, the local minimization problem and slow convergence speed of traditional BP neural network have become the focus of attention.…”