Fuzzy logic discriminant function for evaluating goats exposed to verminosis occurrence regarding resistance, resilience, or sensitivity to parasitism

• Autor(a) principal: Oliveira, Wellhington Paulo da Silva
• Data de Publicação: 2023
• Outros Autores: Santos, Natanael Pereira da Silva, de Oliveira, Max Brandão, Evangelista, Amauri, da Costa filho, Raimundo Tomaz, de Araújo, Adriana Mello
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Ciência animal brasileira (Online)
• Texto Completo: https://revistas.ufg.br/vet/article/view/74727

Worm infections pose a significant challenge to goat farming in the tropics. While individual variations in the animals' response to this disease are observed, understanding its genetic component is crucial for establishing effective herd production management, prioritizing the selection of goats with higher resistance to parasitism. This study aimed to assess goat response to worm infection under natural field conditions using data on eggs per gram of feces (EPG), body condition score (BCS), and conjunctival mucosa coloration (FAMACHA©). Cluster analysis and artificial intelligence (AI) techniques were applied to 3,839 data points from 200 individuals in an experimental goat herd in Piauí, Brazil. The study considered the phenotypic expression of resistance, sensitivity, and resilience to worm infection as responses to parasitism. Three clustering methods, namely Ward, Average, and k-means, were employed and compared with Fuzzy logic obtained through the CAPRIOVI web software. The analysis revealed statistically significant differences (P<0.05) between the groups of animals classified as resistant, resilient, and sensitive to parasitism. Pregnancy and peripartum were identified as stages of heightened sensitivity to parasitism (P<0.05). Among the clustering techniques, traditional statistical methods exhibited excellent performance, with an overall accuracy percentage exceeding 90.00%. In contrast, CAPRIOVI's fuzzy logic demonstrated lower overall accuracy (77.00%). The clustering methods showed similar efficiency, but differed in terms of the distribution of animals per group, with a tendency towards greater numbers in the resistant category. Fuzzy logic circumvented this limitation by enabling the formation of groups tailored to meet the producer's interests, adding consistency in terms of the animals' response to worm infection. This finding highlights the potential of the software for goat health management.Keywords: artificial intelligence; body condition; discriminant analysis; FAMACHA©