Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/129360
Título: Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination
Autores/as: Engel Manchado, Javier Carlos 
Montoya Alonso, José Alberto 
Doménech, Luis
Monge Utrilla, Oscar
Reina Doreste, Yamir 
Matos Rivero, Jorge Isidoro 
Caro Vadillo,Alicia 
García Guasch,Laín 
Redondo, José Ignacio
Clasificación UNESCO: 310904 Medicina interna
Palabras clave: Anamnesis
Clinical Diagnosis
Dog
Machine Learning
Myxomatous Mitral Valve Disease, et al.
Fecha de publicación: 2024
Publicación seriada: Veterinary Sciences 
Resumen: Myxomatous mitral valve disease (MMVD) is a prevalent canine cardiac disease typically diagnosed and classified using echocardiography. However, accessibility to this technique can be limited in first-opinion clinics. This study aimed to determine if machine learning techniques can classify MMVD according to the ACVIM classification (B1, B2, C, and D) through a structured anamnesis, quality of life survey, and physical examination. This report encompassed 23 veterinary hospitals and assessed 1011 dogs for MMVD using the FETCH-Q quality of life survey, clinical history, physical examination, and basic echocardiography. Employing a classification tree and a random forest analysis, the complex model accurately identified 96.9% of control group dogs, 49.8% of B1, 62.2% of B2, 77.2% of C, and 7.7% of D cases. To enhance clinical utility, a simplified model grouping B1 and B2 and C and D into categories B and CD improved accuracy rates to 90.8% for stage B, 73.4% for stages CD, and 93.8% for the control group. In conclusion, the current machine-learning technique was able to stage healthy dogs and dogs with MMVD classified into stages B and CD in the majority of dogs using quality of life surveys, medical history, and physical examinations. However, the technique faces difficulties differentiating between stages B1 and B2 and determining between advanced stages of the disease
URI: http://hdl.handle.net/10553/129360
ISSN: 2306-7381
DOI: 10.3390/vetsci11030118
Fuente: Veterinary Sciences[ISSN2306-7381], v.11(3)
Colección:Artículos
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