Classification of healthy and mastitis Murrah buffaloes by application of neural network models using yield and milk quality parameters

This paper describes a Neural Network (NN) model to classify healthy and mastitis Murrah buffaloes using pH, electrical conductivity, dielectric constant and yield of milk as input parameters and California Mastitis Test (CMT) score as the output parameter. The purpose of this study was to develop such a cost-effective and intelligent classification model, which would serve an alternative to the prevailing Somatic Cell Count (SCC) based techniques to detect mastitis in Murrah buffaloes, because the latter techniques are sophisticated, lengthy and time consuming as well as necessary instruments for carrying out the tests are not, generally, available at the grassroots level or to the small dairy holders. Accordingly, a total of 534 milk samples were collected from 100 lactating Murrah buffaloes, which were scrutinized for mastitis using CMT. The animals were classified into three categories, i.e., healthy, subclinical and clinical mastitis buffaloes and assigned CMT scores as 1, 2, and 3, respectively. The NN models were based on error back propagation learning algorithm with Bayesian regularization mechanism and various combinations of internal parameters. The performance of NN models was compared with that of conventional Multiple Linear Regression (MLR) models also developed in this study. The classification accuracy achieved by the best NN model was 8.02 Root Mean Square percent error (%RMS) while that attained by MLR model was 26.47 %RMS. Further, for classifying healthy vs. subclinical mastitis Murrah buffaloes, sensitivity, specificity and Diagnostic Odds Ratio (DOR) with the best NN model was found to be 98%, 97.72% and 54.87, respectively, having Area under Relative Operating Characteristic (ROC) Curve (AUC) as 0.96 vis-à-vis MLR model attaining the same as 58.87%, 76.72%, and 52.26, respectively, and AUC as 0.81. In case of classifying healthy vs. clinical mastitis Murrah buffaloes, the best NN model achieved sensitivity, specificity and DOR as 99%, 97.28% and 57.92, respectively, with AUC as 0.98 while that with MLR model were determined as 69.23%, 78.20% and 55.46, respectively, and AUC as 0.87. Evidently, the NN model outperformed classical MLR model, in this study. Hence, it can be deduced that NN paradigm has potential to efficiently detect healthy and mastitis Murrah buffaloes on the basis of milk yield and milk quality parameters.