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Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats
Journal article   Open access   Peer reviewed

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats

Abdullah Awaysheh, Jeffrey Wilcke, François Elvinger, Loren Rees, Weiguo Fan and Kurt L Zimmerman
Journal of veterinary diagnostic investigation, Vol.28(6), pp.679-687
11/2016
DOI: 10.1177/1040638716657377
PMID: 27698168
url
https://doi.org/10.1177/1040638716657377View
Published (Version of record) Open Access

Abstract

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats.
 Cats Lymphoma - diagnosis Blood Chemical Analysis - veterinary Cat Diseases - etiology Cat Diseases - diagnosis Inflammatory Bowel Diseases - diagnosis Blood Cell Count - veterinary Male Machine Learning Diagnostic Techniques and Procedures - veterinary Lymphoma - veterinary Algorithms Animals Lymphoma - etiology Bayes Theorem Female Decision Trees Inflammatory Bowel Diseases - etiology Inflammatory Bowel Diseases - veterinary Neural Networks (Computer)

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