DETERMINATION OF CRYPTOSPORIDIUM SPP. RISK FACTORS USING MULTILAYER PERCEPTRON NEURAL NETWORK AND RADIAL BASED FUNCTIONAL ARTIFICIAL NEURAL NETWORK METHOD

Year 2020, Volume: 5 Issue: 2, 83 - 87, 31.12.2020

Ulku Karaman , İpek Balıkçı Çiçek

Abstract

Aim: In the study, it is aimed to compare the estimates of Multilayer artificial neural network (MLPNN) and radial based function artificial neural network (RBFNN) methods, which are among the artificial neural network models in the presence and absence of Cryptosporidium spp., and to determine the factors associated with parasite.

Materials and Methods: In the study, "Cryptosporidium spp. Dataset," the data set named was obtained from Ordu University. In order to classify the presence and absence of Cryptosporidium spp, MLPNN, and RBFNN methods, which are among the artificial neural network models, were used. The classification performance of the models was evaluated with accuracy from the classification performance criteria.

Results: The accuracy, which is the performance criterion obtained with MLPNN, was obtained as 75% of the applied models. The accuracy, which is the performance criterion obtained with the RBFNN model, was achieved as 71.4%. When the effects of variables in the data set in this study on the presence and absence of Cryptosporidium spp. are examined, the three most important variables for the MLPNN model were nausea-vomiting, General Puriri, and sex, respectively. For the RBFNN model, age was obtained as cancer and General Puriri.

Conclusion: It was seen that MLPNN and RBFNN models used in this study gave successful predictions in classifying the presence and absence of Cryptosporidium spp.

Keywords

Multilayer perceptron neural network, Radial-based function neural network, classification, Cryptosporidium spp., risk factors

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