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 colo...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-2615/15/5/737 |
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| author | Oswaldo Margarito Torres-Chable Cem Tırınk Rosa Inés Parra-Cortés Miguel Ángel Gastelum Delgado Ignacio Vázquez Martínez Armando Gomez-Vazquez Aldenamar Cruz-Hernandez Enrique Camacho-Pérez Dany Alejandro Dzib-Cauich Uğur Şen Hacer Tüfekci Lütfi Bayyurt Hilal Tozlu Çelik Ömer Faruk Yılmaz Alfonso J. Chay-Canul |
| author_facet | Oswaldo Margarito Torres-Chable Cem Tırınk Rosa Inés Parra-Cortés Miguel Ángel Gastelum Delgado Ignacio Vázquez Martínez Armando Gomez-Vazquez Aldenamar Cruz-Hernandez Enrique Camacho-Pérez Dany Alejandro Dzib-Cauich Uğur Şen Hacer Tüfekci Lütfi Bayyurt Hilal Tozlu Çelik Ömer Faruk Yılmaz Alfonso J. Chay-Canul |
| author_sort | Oswaldo Margarito Torres-Chable |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fda4ee6ede4841b5b0bc53e4946b4402 |
| institution | DOAJ |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-fda4ee6ede4841b5b0bc53e4946b44022025-08-20T02:59:07ZengMDPI AGAnimals2076-26152025-03-0115573710.3390/ani15050737Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey SheepOswaldo Margarito Torres-Chable0Cem Tırınk1Rosa Inés Parra-Cortés2Miguel Ángel Gastelum Delgado3Ignacio Vázquez Martínez4Armando Gomez-Vazquez5Aldenamar Cruz-Hernandez6Enrique Camacho-Pérez7Dany Alejandro Dzib-Cauich8Uğur Şen9Hacer Tüfekci10Lütfi Bayyurt11Hilal Tozlu Çelik12Ömer Faruk Yılmaz13Alfonso J. Chay-Canul14Division Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, MexicoDepartment of Animal Science, Faculty of Agriculture, Igdir University, TR76000 Iğdır, TürkiyeÁrea de Ciencias Agropecuarias, Grupo de Investigación en Ciencia Animal, Universidad de Ciencias Aplicadas y Ambientales U.D.C.A, Bogotá CP 111166, ColombiaDivision Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, MexicoDivision Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, MexicoDivision Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, MexicoDivision Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, MexicoFacultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes s/n, Mérida CP 97203, Yucatán, MexicoTecnológico Nacional de México, Instituto Tecnológico Superior de Calkiní, Av. Ah-Canul, Calkiní CP 24900, Campeche, MexicoDepartment of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, TR55139 Samsun, TürkiyeDepartment of Animal Science, Faculty of Agriculture, Yozgat Bozok University, TR66000 Yozgat, TürkiyeDepartment of Animal Science, Faculty of Agriculture, Gaziosmanpaşa University, TR60250 Tokat, TürkiyeDepartment of Food Processing, Vocational School of Ulubey, Ordu University, TR52850 Ulubey, TürkiyeDepartment of Animal Science, Faculty of Agriculture, Ondokuz Mayis University, TR55139 Samsun, TürkiyeDivision Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, MexicoThe 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.https://www.mdpi.com/2076-2615/15/5/737FAMACHA©anemiasupport vector machinemachine learningclassification |
| spellingShingle | Oswaldo Margarito Torres-Chable Cem Tırınk Rosa Inés Parra-Cortés Miguel Ángel Gastelum Delgado Ignacio Vázquez Martínez Armando Gomez-Vazquez Aldenamar Cruz-Hernandez Enrique Camacho-Pérez Dany Alejandro Dzib-Cauich Uğur Şen Hacer Tüfekci Lütfi Bayyurt Hilal Tozlu Çelik Ömer Faruk Yılmaz Alfonso J. Chay-Canul Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep Animals FAMACHA© anemia support vector machine machine learning classification |
| title | Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep |
| title_full | Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep |
| title_fullStr | Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep |
| title_full_unstemmed | Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep |
| title_short | Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep |
| title_sort | classification of famacha c scores with support vector machine algorithm from body condition score and hematological parameters in pelibuey sheep |
| topic | FAMACHA© anemia support vector machine machine learning classification |
| url | https://www.mdpi.com/2076-2615/15/5/737 |
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