Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

Publication date: Available online 30 November 2019Source: Preventive Veterinary MedicineAuthor(s): M. Pilar Romero, Yu-Mei Chang, Lucy A. Brunton, Jessica Parry, Alison Prosser, Paul Upton, Eleanor Rees, Oliver Tearne, Mark Arnold, Kim Stevens, Julian A. DreweAbstractIdentifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions within disease causation webs, and enhance our knowledge of TB risk factors and how they are interrelated. Classification tree analysis was used to reveal associations between predictors of TB in England and each of the three surveillance risk areas (High Risk, Edge, and Low Risk) in 2016, identifying the highest risk herds. The main classifying predictor for farms in England overall related to the TB prevalence in the 100 nearest cattle herds. In the High Risk and Edge areas it was the number of slaughterhouse destinations and in the Low Risk area it was the number of cattle tested in surveillance tests. How long ago the last confirmed incident was resolved was the most frequent classifier in trees; if within two years, leading to the highest risk group of herds in the High Risk and Low Risk areas. At least two different slaughterhouse destinations led to the highest risk group of herds in England, whereas in the Edge area it was a combination of no contiguous low-risk...
Source: Preventive Veterinary Medicine - Category: Veterinary Research Source Type: research