Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep
Yazarlar (15)
Universidad Juárez Autónoma De Tabasco, Meksika
Iğdır Üniversitesi, Türkiye
Universidad De Ciencias Aplicadas Y Ambientales, Kolombiya
Universidad Juárez Autónoma De Tabasco, Meksika
Universidad Juárez Autónoma De Tabasco, Meksika
Universidad Juárez Autónoma De Tabasco, Meksika
Universidad Juárez Autónoma De Tabasco, Meksika
Universidad Autónoma De Yucatán, Meksika
Tecnológico Nacional De México, Meksika
Ondokuz Mayis Üniversitesi, Türkiye
Bozok Üniversitesi, Türkiye
Dr. Öğr. Üyesi Lütfi BAYYURT
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Ordu Üniversitesi, Türkiye
Ondokuz Mayis Üniversitesi, Türkiye
Universidad Juárez Autónoma De Tabasco, Meksika
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Animals |
| Dergi ISSN | 2076-2615 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 03-2025 |
| Cilt No | 15 |
| Sayı | 5 |
| DOI Numarası | 10.3390/ani15050737 |
| Makale Linki | https://doi.org/10.3390/ani15050737 |
| 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 | |
| WoS | 1 |
| SCOPUS | 1 |
| Google Scholar | 2 |