Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep
Yazarlar (15)
Oswaldo Margarito Torres-Chable Universidad Juárez Autónoma De Tabasco
Cem Tırınk Iğdır Üniversitesi, Türkiye
Rosa Inés Parra-Cortés Universidad De Ciencias Aplicadas Y Ambientales
Miguel ángel Gastelum Delgado Universidad Juárez Autónoma De Tabasco
Ignacio Vázquez Martínez Universidad Juárez Autónoma De Tabasco
Armando Gomez Vazquez Universidad Juárez Autónoma De Tabasco
Aldenamar Cruz Hernandez Universidad Juárez Autónoma De Tabasco
Enrique Camacho Pérez Universidad Autónoma De Yucatán
Dany Alejandro Dzib Cauich Tecnológico Nacional De México
Uğur Şen Ondokuz Mayis Üniversitesi, Türkiye
Hacer Tüfekci Bozok Üniversitesi, Türkiye
Dr. Öğr. Üyesi Lütfi BAYYURT Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Hilal Tozlu Çelik Ordu Üniversitesi, Türkiye
Ömer Faruk Yılmaz Ondokuz Mayis Üniversitesi, Türkiye
Alfonso J Chay Canul Universidad Juárez Autónoma De Tabasco
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Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Animals (Q1) | ||
| Dergi ISSN | 2076-2615 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 03-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 04-03-2025 |
| Cilt / Sayı / Sayfa | 15 / 5 / 737–0 | DOI | 10.3390/ani15050737 |
| Makale Linki | https://doi.org/10.3390/ani15050737 | ||
| UAK Araştırma Alanları |
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| Özet |
| 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. |
| Anahtar Kelimeler |
| anemia | classification | FAMACHA© | machine learning | support vector machine |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Yöksis | 1 |
| Web of Science | 2 |
| Scopus | 2 |