Tree-Based Methods as an Alternative to Logistic Regression in Revealing Risk Factors of Crib-Biting in Horses

Journal of Equine Veterinary Science

Volumn 30, Issue 1, 2010, Pages 21-26

  Nagy, Krisztina ^a   Reiczigel, Jeno ^a   Harnos, Andrea ^a   Schrott, Anikó ^a   Kabai, Péter ^a  

^a SZENT ISTVÁN UNIVERSITY   (Hungary)

Author keywords

Classification tree; Conditional inference tree; Crib biting; Logistic regression; Risk factors

Indexed keywords

EQUIDAE;

EID: 73649138933     PISSN: 07370806     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jevs.2009.11.005     Document Type: Article

References (20)

1. Albright J.D., Mohammed H.O., Heleski C.R., Wickens C.L., and Houpt K.A. Crib-biting in US horses: breed predispositions and owner perceptions of aetiology. Equine Vet J 41 (2009) 455-458
2. Bachman I., Audigé L., and Stauffacher M. Risk factors associated with behavioural disorders of crib-biting, weaving and box-walking in Swiss horses. Equine Vet J 35 (2003) 158-163
3. Parker M., Goodwin D., and Redhead E.S. Survey of breeders' management of horses in Europe, North America and Australia: comparison of factors associated with the development of abnormal behaviour. Appl Anim Behav Sci 114 (2008) 206-215
4. Waters A.J., Nicol C.J., and French N.P. Factors influencing the development of stereotypic and redirected behaviours in young horses: findings of a four year prospective epidemiological study. Equine Vet J 34 (2002) 572-579
5. nd ed. (2004), Wiley, New York, NY
6. Kass G. An exploratory technique for investigating large quantities of categorical data. Appl Stat 29 (1980) 119-127
7. Kurt I., Ture M., and Kurum A.T. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expet Syst Appl 34 (2008) 366-374
8. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and regression trees (1984), Chapman & Hall, New York, NY
9. Hothorn T., Hornik K., and Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 15 (2006) 651-674
10. Worth A.P., and Cronin M.T. The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. J Mol Struct (Theochem) 622 (2003) 97-111
11. Ho S.H., Jee S.H., Lee J.E., and Park J.S. Analysis on risk factors for cervical cancer using induction technique. Expet Syst Appl 27 (2004) 97-105
12. Harper P.R. A review and comparison of classification algorithms for medical decision making. Health Policy 71 (2005) 315-331
13. Thomas S., Leese M., Walsh E., McCrone P., Moran P., Burns T., et al. A comparison of statistical models in predicting violence in psychotic illness. Compr Psychiatry 46 (2005) 296-303
14. Low M., Joy M.K., and Makan T. Using regression trees to predict patterns of male provisioning in the stitchbird (hihi). Anim Behav 71 (2006) 1057-1068
15. Kubinyi E., Turcsán B., and Miklósi A. Dog and owner demographic characteristics and dog personality trait associations. Behav Processes 81 (2009) 392-401
16. Nagy K., Schrott A., and Kabai P. Possible influence of neighbours on stereotypic behaviour in horses. Appl Anim Behav Sci 111 (2008) 321-328
17. R Development Core Team. R, A language and environment for statistical computing (2007), R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org Available at
18. Berzal F., Cubero J.C., Cuenca F., and Martín-Bautista M.J. On the quest for easy-to-understand splitting rules. Data and knowledge. Engineering 44 (2003) 31-48
19. National Joint Equine Welfare Committee and the R.S.P.C.A. Code of practice for tethering horses and ponies. Available at: http://www.rspca.org.uk/.
20. McBride S., and Hemmings A. A neurologic perspective of equine stereotypy. J Equine Vet Sci 29 (2009) 10-16