Szlosek D, Coyne M, Riggott J, Knight K, McCrann DJ, Kincaid D. Development and validation of a machine learning model for clinical wellness visit classification in cats and dogs. Front Vet Sci. 2024;11. doi:10.3389/fvets.2024.1348162.
This study presents the development of a machine learning model designed to classify veterinary visits as wellness or other types, aiding in early disease detection in asymptomatic cats and dogs. The Gradient Boosting Machine model was trained on 11,105 clinical visits and validated against manual classification by three board-certified veterinarians. The model achieved a specificity of 0.94 and sensitivity of 0.86, with an overall balanced accuracy of 0.90. These results suggest the model's potential for accurately identifying wellness visits, with further validation in prospective studies recommended.
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