Hello, everyone, and welcome to our presentation. My name is Dr Andy Mauromoustakos. I'm a professor at the Agricultural Statistics Lab at the University of Arkansas. My co- presenter is Dr Shawna Weimer . She's A ssistant Professor at Poultry Science Department at the University of Arkansas, and she's the Animal Welfare chairperson at the university.
We're going to talk to you today about predictive models for BCO lameness using health and stress and leg health parameters in broilers. Our presentation is going to be short. We're going to discuss the models that we are fitting, the champion models, the ones that they get the medals. We're going to evaluate them, and we're going to have some conclusions in the end. Shawna?
All right. This study compared physiological and non-invasive measures of stress and lameness in clinically healthy and BCO— or b acterial chondronecrosis with osteomyelitis—laden broilers. BCO is a leading cause of infectious lameness in broiler chickens, with flock diagnosis requiring euthanasia. Thus, there's a need for technological innovations to detect health and lameness status in animals to project the disease likelihood prior to clinical onset.
In this study, birds were raised in separate environmental chambers with either wood shavings on the floor, or the litter, and a wire flooring model that is validated to induce BCO lameness. Nine non-invasive measures of stress and lameness were collected from 256 birds, male broilers, over several weeks, which included core body temperature, stress hormone, surface temperatures of the head, the eye, and the beak, and the legs, the hocks, the shank, and the feet, with infrared thermography. Leg blood oxygen saturation was also measured with a pulse oximeter.
Of these measures, two we sought to validate. The first was extraction of corticosterone from the feathers. Corticosterone is the major primary stress hormone, and the gold standard for measures is blood serum concentrations, which requires the capture and restraint of the bird to collect it. So if feather corticosterone could be validated, then we could simply clip a feather and not put the bird through that stress of restraint and blood draw.
The second was the thermal images, of which each pixel has its own temperature recorded and can be used to quantify external changes in skin temperature related to blood flow, offering a non-invasive tool to measure health and welfare. During stress, peripheral blood is shunted to the core, and we expected the average pixels of the eye and the beak, or Eavg and Bavg, to be lower in lame than sound birds, which we correlated with the serum corticosterone.
For the thermal images of the legs, we expected the average pixel temperature of the hock, the shank, and the foot to be lower in the lame birds, both for the stress reasons and also for the colonization of the bacteria that slowed the blood flow, which we correlated with the leg blood oxygen saturation. There were marked differences between lame and sound birds.
Our objectives is to identify which of these nine health and stress indicators are important for lameness. We want to build models, both for prediction purposes, but we are in agriculture, and we like to publish papers that try to explain how our inputs are affect the response. We are hoping that some of the models, the traditional regression models, will do fairly well and we can interpret those.
In our methods, we talked about… It's a balanced study. It's a match paired experiment where every time a sound bird is observed, a lame bird is also observed in the same indicators. We have an incidence of a disease of 0.5. We're going to take advantage of the Model Screening platform of JMP Pro 17. We're going to select the lame that has values 1 and 0 categorical. 1 stands for, yes, it's lame. 0 is the sound .
We have our nine predictors in here. We're going to do the defaults. We're going to fit all of the machine learning models, that they are checked. We're going to select cross validation. We're going to have 5-fold cross validation. With our approximate 250 birds, we're going to have about 50 birds per fold, and we're going to repeat it twice. We selected the random seed so we can reproduce the results. Notice that I did not add the quadratic terms and interaction terms to hopefully have easier interpretations.
When we select this, and we click " Run ," JMP takes about a minute. I t's going to come up with the ranking of the models that we have fitted. This is what we call the beginning of the end. We have 10 different data sets that we have tried. This is the average fit criteria, higher the RS quare, and is the ranking the best, second best, third best.
Hopefully for us, we expected that the Neural Boosted model may do a little bit better than the traditional regression models, such as the Penalized Regression and the Logistic Regression models, but we were happy to see that these are our close second.
If we wanted to see how our best model did, we're going to go and see that the best model— the Neural Boosted, that is the best model for predicting purposes —had a misclassification of 0. You can see here both in the training and the validation that our receiver operating characteristic curve reaches very soon the 1 and stays at 1, which is extremely good. We see the confusion metrics that out of the 49 birds, we're not really misclassifying any of them, so this is a very good model with a very high generalized RS quare and all of the other fit criteria that is produced in here.
But we are more interested in the regression type of models. The regression type of models, the Generalized Regression, we can see that when we use Lasso as the estimation method, the model included all of the variables, and we have couple of non -significant variables in the model in here. We can see these non -significant factors, such as the FCORT, you can see it that it's not crossing the horizontal line. You can see that the year average is not significant, but is included. We can see that this model had approximately three misclassification, a misclassification rate of about 7 %.
Here is the third model that was... If we had to give it gold, silver, and bronze, these two will share the second place. The Logistic Regression model, when we did the step wise procedure, decided not to include the two highly non -significant factors. We can see in our regression model, in the Logistic Regression model, that SCORT is the most important variable. This model has similar misclassification rate of three, the same as the Logistic Regression.
Here is how the seven indicator variables are used to predict the probability of lameness. What we like about the regression type of models is that we can get odds ratios that will help us to interpret. For example, for our most important variable, we can see that the odds of lameness is twice with one unit of increase is serum cortisol.
Overall, we would like to say that the Logistic Regression model only used seven out of the nine indicators. The Neural Boosted model and the Generalized Regression, both of them used all nine indicators for lameness. All of the models have area under the curve of greater than 0.9. All of the models have a lower… The regression type of models had the 7 % misclassification on the validation set, and the Neural Boosted did not have anything.
We can compare our three winner models using the Model Comparison platform in JM P. We can see that in terms of predicting, strictly predicting, the Neural Boosted model is significantly better than both of the Generalized Regression and the regression model. The two regression models , the one with all nine variables versus the one with the seven variables, are not significantly different from each other. They all had a very similar area under the curve, and they had similar misclassification of three birds.
This is our presentation. We'd like to thank you for your attention. We have some references that you can find similar related material to the techniques that we used in JMP documentation that will help you through this process. Thank you for your attention.
