Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination
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 acc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Veterinary Sciences |
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| Online Access: | https://www.mdpi.com/2306-7381/11/3/118 |
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| author | Javier Engel-Manchado José Alberto Montoya-Alonso Luis Doménech Oscar Monge-Utrilla Yamir Reina-Doreste Jorge Isidoro Matos Alicia Caro-Vadillo Laín García-Guasch José Ignacio Redondo |
| author_facet | Javier Engel-Manchado José Alberto Montoya-Alonso Luis Doménech Oscar Monge-Utrilla Yamir Reina-Doreste Jorge Isidoro Matos Alicia Caro-Vadillo Laín García-Guasch José Ignacio Redondo |
| author_sort | Javier Engel-Manchado |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T17:46:34Z |
| format | Article |
| id | doaj.art-ce129f0ddcab4a9181971de0c7a47cce |
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| issn | 2306-7381 |
| language | English |
| last_indexed | 2024-04-24T17:46:34Z |
| publishDate | 2024-03-01 |
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| record_format | Article |
| series | Veterinary Sciences |
| spelling | doaj.art-ce129f0ddcab4a9181971de0c7a47cce2024-03-27T14:07:23ZengMDPI AGVeterinary Sciences2306-73812024-03-0111311810.3390/vetsci11030118Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical ExaminationJavier Engel-Manchado0José Alberto Montoya-Alonso1Luis Doménech2Oscar Monge-Utrilla3Yamir Reina-Doreste4Jorge Isidoro Matos5Alicia Caro-Vadillo6Laín García-Guasch7José Ignacio Redondo8Internal Medicine, Veterinary Medicine and Therapeutic Research Group, Faculty of Veterinary Science, Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainInternal Medicine, Veterinary Medicine and Therapeutic Research Group, Faculty of Veterinary Science, Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainDepartment of Mathematics, Physics and Technological Sciences, Higher School of Technical Education, Cardenal Herrera-CEU University, 46115 Valencia, SpainCardiology Service, Mediterráneo Veterinary Hospital, Evidensia IVC, 28007 Madrid, SpainCardiology Service, IVC Evidensia, Los Tarahales Veterinary Hospital, 35019 Las Palmas de Gran Canaria, SpainInternal Medicine, Veterinary Medicine and Therapeutic Research Group, Faculty of Veterinary Science, Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainDepartment of Animal Medicine and Surgery, Faculty of Veterinary Medicine, Complutense University, 28040 Madrid, SpainInternal Medicine, Veterinary Medicine and Therapeutic Research Group, Faculty of Veterinary Science, Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainDepartment of Animal Medicine and Surgery, Faculty of Veterinary Medicine, Cardenal Herrera-CEU University, 46115 Valencia, SpainMyxomatous 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.https://www.mdpi.com/2306-7381/11/3/118anamnesisclinical diagnosismachine learningpredictive modelmyxomatous mitral valve diseasedog |
| spellingShingle | Javier Engel-Manchado José Alberto Montoya-Alonso Luis Doménech Oscar Monge-Utrilla Yamir Reina-Doreste Jorge Isidoro Matos Alicia Caro-Vadillo Laín García-Guasch José Ignacio Redondo Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination Veterinary Sciences anamnesis clinical diagnosis machine learning predictive model myxomatous mitral valve disease dog |
| title | Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination |
| title_full | Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination |
| title_fullStr | Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination |
| title_full_unstemmed | Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination |
| title_short | Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination |
| title_sort | machine learning techniques for canine myxomatous mitral valve disease classification integrating anamnesis quality of life survey and physical examination |
| topic | anamnesis clinical diagnosis machine learning predictive model myxomatous mitral valve disease dog |
| url | https://www.mdpi.com/2306-7381/11/3/118 |
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