New approach based on fuzzy classification of the serological tests “ELISA” for the diagnosis of cattle tuberculosis

Several antigens have been produced and/or secreted from the mycobacterium bovis. The agent of bovine tuberculosis (TB) is used to detect this disease in cattle through the serological ELISA test (a, b, c, d and e). In this work, we propose a novel approach to improve the diagnosis bovine tuberculosis. In order to select the antigens with top priority, the proposed methodology is based on a comparison of their power of characterization. Experimental results are analyzed using two categories: sick (TB +) and not sick (TB -). By extracting some original features and thanks to the unsupervised Fuzzy C-Means (FCM) classification, 89% is achieved as classification accuracy. Compared to previous works, the proposed expert system is very promising and helpful for the veterinary diagnosis of tuberculosis.