engleski

Classification accuracy of algorithms for blood chemistry data of three aquaculture-influenced marine fish species

The aim of this study was determination and discrimination of biochemical data between three aquaculture-influenced marine fish species (sea bass, Dicentrarchus labrax ; sea bream, Sparus aurata L ; mullet, Mugil spp.) based on machine learning methods. The approach relying on machine learning methods gives more usable classification solutions and provides better insight into the collected data. So far, these new methods were applied to the problem of discrimination of blood chemistry data with respect to season and feed of one single species. This is the first time that these classification algorithms were used as a framework for rapid differentiation between three fish species. Among the machine learning methods used, decision trees provided the clearest model, which correctly classified 210 samples or 85.71 %, and incorrectly classified 35 samples or 14.29 % and clearly identified three investigated species regarding to their biochemical traits.

machine learning techniques; sea bass; sea bream; mullet; plasma biochemistry

nije evidentirano