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Open Access Research article

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology

Manuel J Sanchez-Vazquez1, Mirjam Nielen2, Sandra A Edwards3, George J Gunn1 and Fraser I Lewis4*

Author Affiliations

1 Scottish Agricultural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK

2 Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands

3 Newcastle University, Agriculture Building, Newcastle upon Tyne, NE1 7RU, Newcastle, UK

4 Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland

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BMC Veterinary Research 2012, 8:151  doi:10.1186/1746-6148-8-151

Published: 31 August 2012

Abstract

Background

Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously.

Results

Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology.

Conclusions

The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.