Abstract
Since renal cell carcinoma (RCC) is a very common renal disease and the number of diseases constantly increases, it is very important to prevent this malignity by making an appropriate diagnosis. In this study, artificial neural networks are used to identify kidney cancer based on parameters that have the most detrimental effects on kidney’s health: urine volume, urine color, urine sediment, blood in urine, urine pH, creatinine, creatinine clearance, urea, uric acid, LDH, CEA, C3, C4, CH50, haptoglobin, and fibrinogen.
Collected data derives from 300 patients, 100 of them healthy (Group 1) and 200 diseased (Group 2). Group 1 didn’t have any abnormality of these parameters. In the case of Group 2, the parameters of every respondent were not normal, which means that the value of parameters is higher or lower than reference values of the same parameters. The result of those abnormalities is kidney cancer.
2, 5, and 15 neurons were tested during the development. An Artificial Neural Network with 15 neurons shows the best results with accuracy training 99,01% and accuracy validation 98,62%. After Artificial Neural Network performance validation accuracy was 93,3%, sensitivity 90%, and specificity 95,0%. Accuracy helps us to separate healthy and diseased and also helps to recognize the stage of the disease. Although the use of ANN has long been present in urology and even diagnostics in kidney diseases, this paper tried to give its contribution in the form of the development of ANN with the best possible parameters of specificity and sensitivity.
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Turajlić, A., Turković, H., Tursunović, M., Vatreš, A., Vehabović, N., Viteškić, M. (2021). Using Artificial Network for Identification of Kidney Cancer. In: Badnjevic, A., Gurbeta Pokvić, L. (eds) CMBEBIH 2021. CMBEBIH 2021. IFMBE Proceedings, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-73909-6_40
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