title: What variables are important in predicting bovine viral diarrhea virus? A random forest approach.
abstract: Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.
authors.affiliation: Laboratory of Veterinary Epidemiology, Faculty of Veterinary, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9090, CEP 91540-000, Porto Alegre, RS, Brazil. gustavoetal@gmail.com.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Machado
authors.affiliation: Experimental and Molecular Cardiovascular Laboratory, Experimental Research Center, Hospital de Clínicas de Porto Alegre (HCPA), Av. Ramiro Barcelos, 2350, CEP 99010-115, Porto Alegre, RS, Brazil. mari.mendoza@gmail.com.
authors.forename: Mariana Recamonde
authors.initials: MR
authors.lastname: Mendoza
authors.affiliation: Laboratory of Veterinary Epidemiology, Faculty of Veterinary, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9090, CEP 91540-000, Porto Alegre, RS, Brazil. lgcorbellini@hotmail.com.
authors.forename: Luis Gustavo
authors.initials: LG
authors.lastname: Corbellini
mesh_terms: D000465:Algorithms; D000818:Animals; D001912:Bovine Virus Diarrhea-Mucosal Disease / Q000453:epidemiology* / Q000821:virology; D002417:Cattle; D003430:Cross-Sectional Studies; D003663:Decision Trees; D001908:Diarrhea Viruses, Bovine Viral / Q000502:physiology*; D016015:Logistic Models; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods