RT Journal Article
T1 Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination
A1 Engel-Manchado, Javier
A1 Montoya-Alonso, José Alberto
A1 Doménech, Luis
A1 Monge-Utrilla, Óscar
A1 Reina Doreste, Yamir
A1 Matos Rivero, Jorge Isidoro
A1 Caro Vadillo, Alicia
A1 García-Guasch, Laín
A1 Redondo, José Ignacio
AB 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.
PB MDPI
SN 2306-7381
YR 2024
FD 2024-03-06
LK https://hdl.handle.net/20.500.14352/103169
UL https://hdl.handle.net/20.500.14352/103169
LA eng
NO Engel-Manchado, J.; Montoya-Alonso, J.A.; Doménech, L.; Monge-Utrilla, O.; Reina-Doreste, Y.; Matos, J.I.; Caro-Vadillo, A.; García-Guasch, L.; Redondo, J.I. Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination. Vet. Sci. 2024, 11, 118. https://doi.org/ 10.3390/vetsci11030118
NO Author Contributions: J.E.-M. and J.I.R.: study design, analysis, and interpretation of data; drafting of the manuscript; data analysis: J.I.R. and L.D., J.E.-M., J.I.R., J.A.M.-A., O.M.-U., Y.R.-D., J.I.M., A.C.-V. and L.G.-G.: revision of the manuscript. All authors participated in the discussion of the results, corrected, read, and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
DS Docta Complutense
RD 6 abr 2025