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.

Link to Article: Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats - PubMed (nih.gov)

This article was summarized by an AI tool that uses natural language processing. The tool is not perfect and may make mistakes or produce inaccurate or irrelevant information, but is reviewed by the post’s author prior to publishing. If you want to learn more about the article, please refer to the original source that is cited at the end of the article.