Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle

Eman A. Abo Elfadl^1, and Fatma D. M. Abdallah²

¹Department of Animal Husbandry and Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Egypt

²Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt

Cite this paper:
Eman A. Abo Elfadl and Fatma D. M. Abdallah. Using Discriminant Analysis and Artificial Neural Network Models for Classification and Prediction of Fertility Status of Friesian Cattle. American Journal of Applied Mathematics and Statistics. 2017; 5(3):90-94. doi: 10.12691/ajams-5-3-1

Abstract

Background & objectives: This study was undertaken to compare the accuracies of Discriminant analysis model (DA) and Artificial neural networks model (ANN) for classification and prediction of Friesian cattle fertility status by using its reproductive traits. Methods: Data was collected through field survey of 2843 animal records of Friesian breed belongs to El Dakhalia province farms, Egypt. Data was covering the period extended from 2010 to 2013. The samples of dairy production sectors were selected randomly. Data was collected from valid farm records or the structured questionnaires established by the researcher. Results: The results of classification accuracy indicated that the artificial neural network (ANN) model is more efficient than the discriminant analysis (DA) model in expressing overall classification accuracy and accuracies of correctly classified cases of fertility status for Friesian cattle. The results showed that The ANN models had shown the highest classification accuracy (93.6%) for year (2010) while, it was (79.9%) for DA. The comparison of overall classification accuracies clearly favored the supremacy of ANN over DA. The results also were confirmed by the areas under Receiver Operating Characteristic Curves (ROC) captured by ANN and DA. ROC curves are used mainly for comparing different discriminating rates. Areas under ROC curves were higher in case of ANN models across the different years compared to DA models. The differences in accuracies were also significant at 5% level of significance with p-value 0.005 by using Paired Sample t-test. From all of the above we can conclude that artificial neural network model was more accurate in prediction and classification of fertility status than a traditional statistical model (Discriminant analysis).

Keywords:
artificial neural networks discriminant analysis prediction classification ROC curve and fertility status

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Iyer E., Murti V., K. and Arora, V, “Comparison of Artificial Neural Network and Multiple Discriminant Analysis Models for Bankruptcy Prediction in India”. IJAPRR International Peer Reviewed Refereed Journal, 3(1):12-21, 2016.
[2]	Hecht-Nielsen R, “Cogent confabulation”. Neural Networks, 18: 111-115, 2005.
[3]	Schumann, A, “Neural Networks versus Statistics: A Comparing Study OF Their Classification Performance on Well Log Data”. Free University of Berlin, Geoinformatics. Germany, 12249 Berlin, Malteserstr, 1997, 74-100.
[4]	Gimblett, R.H. and G. L. Ball, “Neural network architectures for monitoring and simulating changes in forest resources management”. AI Applications, 9: 103-123, 1995.
[5]	Alvarez, S, “Generation of terrain textures using neural networks”. Unpublished master thesis. Department of Computer Science, Colorado State University, 1995, 44.
[6]	Guan, B. T. and G. Gertner, “Modeling individual tree survival probability with a random optimization procedure”: An artificial neural network approach. AI Application, 9: 39-52, 1995.
[7]	Harvey, W., Dean, D.J, “Computer-aided visualization of proposed road networks using GIS and neural networks”. In: First Southern Forestry GIS Conference. G.J. Arthaud, W.C. Hubbard (Eds.). University of Georgia, Athens, GA, 1996, 416-417.
[8]	Hair J., Black W. C., Babin B. J., Anderson, R. E., and Tatham, R. L, “Multivariate Data Analysis”, 6th edition, Logistic Regression: Regression with a Binary Dependent Variable, 2009, 379.
[9]	Yoon, Y., Swales, G., Margavio, T.M., “A comparison of discriminant analysis versus artificial neural networks”. J. Oper. Res. Soc., 44 (1): 51-60.1993.
[10]	Marzban, C., Paik, H., Stumpf, G, “Neural networks versus Gaussian discriminant analysis”. AI Appl., 11 (1): 49-58.1997.
[11]	Blackard, J.A., Dean, D.J., “Evaluating integrated artificial neural network and GIS techniques for predicting likely vegetative cover types”. In: First Southern Forestry GIS Conference. G.J. Arthaud, W.C. Hubbard (Eds.). University of Georgia, Athens, GA, 1996, 416-417.