title: Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.
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.
authors.affiliation: Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VAPopulation Health Sciences (Elvinger), Virginia Tech, Blacksburg, VABusiness Information Technology (Rees), Virginia Tech, Blacksburg, VAAccounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA.
authors.forename: Abdullah
authors.initials: A
authors.lastname: Awaysheh
authors.affiliation: Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VAPopulation Health Sciences (Elvinger), Virginia Tech, Blacksburg, VABusiness Information Technology (Rees), Virginia Tech, Blacksburg, VAAccounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Wilcke
authors.affiliation: Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VAPopulation Health Sciences (Elvinger), Virginia Tech, Blacksburg, VABusiness Information Technology (Rees), Virginia Tech, Blacksburg, VAAccounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA.
authors.forename: François
authors.initials: F
authors.lastname: Elvinger
authors.affiliation: Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VAPopulation Health Sciences (Elvinger), Virginia Tech, Blacksburg, VABusiness Information Technology (Rees), Virginia Tech, Blacksburg, VAAccounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA.
authors.forename: Loren
authors.initials: L
authors.lastname: Rees
authors.affiliation: Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VAPopulation Health Sciences (Elvinger), Virginia Tech, Blacksburg, VABusiness Information Technology (Rees), Virginia Tech, Blacksburg, VAAccounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA.
authors.forename: Weiguo
authors.initials: W
authors.lastname: Fan
authors.affiliation: Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VAPopulation Health Sciences (Elvinger), Virginia Tech, Blacksburg, VABusiness Information Technology (Rees), Virginia Tech, Blacksburg, VAAccounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA kzimmerm@vt.edu.
authors.forename: Kurt L
authors.initials: KL
authors.lastname: Zimmerman
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001772:Blood Cell Count / Q000662:veterinary; D001774:Blood Chemical Analysis / Q000662:veterinary; D002371:Cat Diseases / Q000175:diagnosis* / Q000209:etiology; D002415:Cats; D003663:Decision Trees; D019937:Diagnostic Techniques and Procedures / Q000662:veterinary*; D005260:Female; D015212:Inflammatory Bowel Diseases / Q000175:diagnosis / Q000209:etiology / Q000662:veterinary*; D008223:Lymphoma / Q000175:diagnosis / Q000209:etiology / Q000662:veterinary*; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer
keywords: Cats; diagnosis; inflammatory bowel disease; lymphoma; machine learning