Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep

The aim of this study is to evaluate the model performance in the classification of FAMACHA© scores using Support Vector Machines (SVMs) with a focus on the estimation of the FAMACHA© scoring system used for early diagnosis and treatment management of parasitic infections. FAMACHA© scores are a color-based visual assessment system used to determine parasite load in animals, and in this study, the accuracy of the model was investigated. The model’s accuracy rate was analyzed in detail with metrics such as sensitivity, specificity, and positive/negative predictive values. The results showed that the model had high sensitivity and specificity rates for class 1 and class 3, while the performance was relatively low for class 2. These findings not only demonstrate that SVM is an effective method for classifying FAMACHA© scores but also highlight the need for improvement for class 2. In particular, the high accuracy rate (97.26%) and high kappa value (0.9588) of the model indicate that SVM is a reliable tool for FAMACHA© score estimation. In conclusion, this study demonstrates the potential of SVM technology in veterinary epidemiology and provides important information for future applications. These results may contribute to efforts to improve scientific approaches for the management of parasitic infections.

• Autores: chable, torres; TIRINK, CEM; Parra-Cortés, Rosa Inés; GASTELUM DELGADO, MIGUEL ÁNGEL; Vázquez Martínez, Ignacio; Gómez Vázquez, Armando; Cruz-Hernández, Aldenamar; Camacho-Pérez, Enrique; Dzib Cauich, Dany Alejandro; Bayyurt, Lütfi; TOZLU ÇELİK, HİLAL; Yilmaz, Omer Faruk; Chay-Canul, Alfonso Juventino
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2025
• Institución: Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
• Repositorio: Repositorio Institucional UDCA
• Idioma: eng
• OAI Identifier: oai:repository.udca.edu.co:11158/6344
• Acceso en línea: https://repository.udca.edu.co/handle/11158/6344; https://doi.org/10.3390/ani15050737; https://repository.udca.edu.co/
• Palabra clave: 630 - Agricultura y tecnologías relacionadas::636 - Producción animal; Enfermedades Parasitarias; Máquina de Vectores de Soporte; Anemia; FAMACHA; Aprendizaje automático (Inteligencia artificial)
• Rights: openAccess
• License: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es