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Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination.

Authors :
Engel-Manchado J
Montoya-Alonso JA
Doménech L
Monge-Utrilla O
Reina-Doreste Y
Matos JI
Caro-Vadillo A
García-Guasch L
Redondo JI
Source :
Veterinary sciences [Vet Sci] 2024 Mar 06; Vol. 11 (3). Date of Electronic Publication: 2024 Mar 06.
Publication Year :
2024

Abstract

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.

Details

Language :
English
ISSN :
2306-7381
Volume :
11
Issue :
3
Database :
MEDLINE
Journal :
Veterinary sciences
Publication Type :
Academic Journal
Accession number :
38535852
Full Text :
https://doi.org/10.3390/vetsci11030118