Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep

SIMPLE SUMMARY: Infection by gastrointestinal nematodes is a major sanitary issue in sheep production. Therefore, improvements in the animal’s health are important to reduce losses and improve animal welfare. Thus, this study investigated the feasibility of using easy-to-measure phenotypic traits to predict sheep resistant, resilient, and susceptible to gastrointestinal nematodes, compared the classification performance of different methods, and evaluated the applicability of the best classification model on each farm. The results revealed the multinomial logistic regression and linear discriminant analysis models presented the best classification performances for the susceptible and resistant animals. The results suggest that the use of readily available records and easily measurable traits may contribute to the identification of susceptible animals, supporting management decisions at the farm level and potentially reducing the economic losses due to parasitic infection. The animals identified as resistant can also be incorporated as selection candidates into breeding programs for the genetic improvement of sheep populations. ABSTRACT: This study investigated the feasibility of using easy-to-measure phenotypic traits to predict sheep resistant, resilient, and susceptible to gastrointestinal nematodes, compared the classification performance of multinomial logistic regression (MLR), linear discriminant analysis (LDA), random forest (RF), and artificial neural network (ANN) methods, and evaluated the applicability of the best classification model on each farm. The database comprised 3654 records of 1250 Santa Inês sheep from 6 farms. The animals were classified into resistant (2605 records), resilient (939 records), and susceptible (110 records) according to fecal egg count and packed cell volume. A random oversampling method was performed to balance the dataset. The classification methods were fitted using the information of age class, the month of record, farm, sex, Famacha© degree, body weight, and body condition score as predictors, and the resistance, resilience, and susceptibility to gastrointestinal nematodes as the target classes to be predicted considering data from all farms randomly. An additional leave-one-farm-out cross-validation technique was used to assess prediction quality across farms. The MLR and LDA models presented good performances in predicting susceptible and resistant animals. The results suggest that the use of readily available records and easily measurable traits may provide useful information for supporting management decisions at the farm level.