Discriminant analysis for the prediction and classification of tick-borne infections in some dairy cattle herds at Dakahlia Governorate, Egypt

[Abstract] This study was undertaken to use the variable loadings in linear discriminant analysis (LDA) to determine the most important predictors for the discrimination of tick-borne diseases (TBDs), particularly babesiosis and anaplasmosis and predict the group membership from the predictors. In total, 163 cattle, from different localities at Dakahlia Governorate, Egypt, were investigated in 2012 and 2013 for the presence of TBDs. All cattle were clinically examined and a clinical index score was determined for each cow. Blood samples were also collected from each animal for adopting microscopy and diagnostic laboratory methods. Out of the examined cattle, 83 animals were acutely-ill (Babesia bovis and Anaplasma marginale were identified in 11 and 10 animals, respectively), while 80 cows were apparently healthy but having previous attacks of blood parasites (23 animals harbored anaplasma marginale (asymptomatic carriers)). The remained 119 animals were negative to TBDs. Fourteen animals were not survived and 149 cases were survived. As the result of the first LDA to discriminate babesiosis, anaplasmosis and negative to TBDs, 89.0% of animals were correctly classified; 78.8% (26/33) for anaplasma, 100% (11/11) for babesia infections, 90.8% (108/119) for negative to TBDs, respectively. The important predictors for the discrimination were oculonasal discharge, bloody feces, hemoglobinuria, bloody feces and respiratory rate. On the other hand, the second LDA discrimination showed high classification accuracy of 87.1% for the discrimination of survivors and non-survivors; 89.9% (134/149) for survivors and 57.1% (8/14) for non-survivors, while the important predictors included oculonasal discharge, recumbent posture and nervous sign.