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Affinity Workshop: Women in Machine Learning

Respiratory Conditions (EIPH, PLH, and Mucus) in Racehorses

Allison Fisher · Warwick Bayly · Sierra Shoemaker · Julia Bagshaw · Yuan Wang · Macarena Sanz


The horse racing industry is a multi-billion dollar industry and thousands of people's livelihood depend on the racing industry. Respiratory diseases and conditions in racehorses are common and noteworthy.The strenuous activity performed by racehorses can exasperate respiratory diseases. Respiratory conditions such as Exercise Induced Pulmonary Haemorrhage (EIPH), Pharyngeal Lymphoid Hyperplasia (PLH) and mucus accumulation can all affect race performance and lead to poor health outcomes.From our previous univariate analysis, we found that EIPH levels differed significantly among the different race tracks within this study. Since, this produced a significant and worthwhile result, we would like to better understand the reasoning behind this difference. We were able to gain significant insight through more traditional statistical methods such as PCA, ordinal logistic regression, MANOVA and bootstrapping for non-parametric summary statistics.For EIPH, PLH, and the mucus score disease severity is expressed through a grading system. For this particular study, each disease state was graded independently by 3 veterinarians. This categorical grading system leads naturally to the use of a logistic regression model. Ordinal Logistic regression can be used to help predict the probability of falling in a certain disease grade given a set of predictor variables. Ordinal logistic regression takes into account the order nature of the response variable. For all three disease states; EIPH, PLH, and mucus accumulation, this is the ideal regression model. For this analysis, we used PCs to further describe our data and to reduce our data-set and find a worthwhile relationship among the factors. This analysis was done to hopefully find a combination of factors that would explain the different levels of the disease state of EIPH. A excellent benefit of using PCA is that it does not require the data to be normally distributed.

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