Journal of Veterinary Clinics (한국임상수의학회지)

The Korean Society of Veterinary Clinics (한국임상수의학회)
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Classification Tree Analysis to Assess Contributing Factors Influencing Biosecurity Level on Farrow-to-Finish Pig Farms in Korea

분류 트리 기법을 이용한 국내 일괄사육 양돈장의 차단방역 수준에 영향을 미치는 기여 요인 평가

  • Kim, Kyu-Wook (College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University) ;
  • Pak, Son-Il (College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University)
  • 김규욱 (강원대학교 수의과대학 및 동물의학종합연구소) ;
  • 박선일 (강원대학교 수의과대학 및 동물의학종합연구소)
  • Received : 2016.02.15
  • Accepted : 2016.04.14
  • Published : 2016.04.30
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Abstract

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

Keywords

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