Haematological parameters of Cyprinus carpio with reference to probiotic feed: A machine learning approach

Abstract

The study aims to analyze the haematological parameters of Cyprinus carpio with reference to the formulation of the probiotic fortified feeds using a machine learning approach. C. carpio fed with pelletized feed, probiotic pelletized feed (5% Lysinibacillus macroides), probiotic pearl beads (5% L. macroides) and probiotic rice puff (5% L. macroides) for 60 days. At the end of the experiments, using blood samples, the haematological indices such as leucocytes, erythrocytes, hemoglobin, hematocrit and packed-cell-volume, were analyzed. Duncan’s Multiple Range Test showed that the haematological parameters in control feeding regimes significantly (P<0.05) were low compared with that of the probiotic feeding regimes. The data sets of different feeding regimes were classified using the machine learning method. In the present study, the classifiers like the Random Forest, the Linear Model, and the Decision Tree were employed. To identify the relationship between the features, correlation coefficient and dendrogram were applied. The results of the machine learning method showed high accuracy (98%) in random forest methods followed by the decision tree method. The correlation coefficient between the haematological indices recorded a positive value. But, calculated values of mean corpuscular volume, mean corpuscular hemoglobin and mean corpuscular haemoglobin concentration were either low positive or negatively correlated with other haematological indices. Based on the results, the Random Forest, Linear Model and Decision Tree Analysis might be considered for haematological classification of the fish haematological data set.