Using ML to differentiate IBD from Lymphoma in Cats

A supervised ML approach evaluating clinicopathologic variables

Evaluation of supervised machinelearning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats

Abdullah Awaysheh, Jeffrey Wilcke, François Elvinger, Loren Rees, Weiguo Fan, Kurt L. Zimmerman

This article explores the use of machine-learning algorithms to differentiate between inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) in cats.

The authors developed three prediction models using three different machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested using data from complete blood count (CBC) and serum chemistry (SC) results from normal cats, cats with IBD, and cats with ALA. The naive Bayes and artificial neural networks models achieved higher classification accuracy compared to the decision tree model.

The area under the receiver-operating characteristic curve for classifying cases into the three categories was 83% for naive Bayes, 79% for decision tree, and 82% for artificial neural networks. The models were able to provide another noninvasive diagnostic tool to assist clinicians in differentiating between IBD and ALA, as well as between diseased and nondiseased cats.

The models used a subset of CBC and SC variables to classify the cats, with the naive Bayes classifier using 10 variables and the artificial neural networks classifier using 4 variables. The authors suggest that the naive Bayes and artificial neural networks classifiers are the best choices for constructing prediction models in this specific use case.

Overall, this study demonstrates the potential of machine-learning algorithms as noninvasive diagnostic tools for differentiating between gastrointestinal diseases in cats.

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