Mahmoud, Samer Mohamed KanaanPerova, IrynaPliss, Iryna2021-07-152021-07-152017Mahmoud, S. M. K., Perova, I., & Pliss, I. (2017). Multidimensional Neo-Fuzzy-Neuron for Solving Medical Diagnostics Tasks in Online-Mode. Journal of Applied Computer Science, 25(1), 39-48. https://doi.org/10.34658/jacs.2017.1.39-481507-0360http://hdl.handle.net/11652/3874https://doi.org/10.34658/jacs.2017.1.39-48In this paper neuro-fuzzy approach for medical data processing is considered. Special capacities for methods and systems of Computational Intelligence were introduced for Medical Data Mining tasks, like transparency and interpretability of obtained results, ability to classify nonconvex and overlapped classes that correspond to various diagnoses, necessity to process data in online mode and so on. Architecture based on the multidimensional neo-fuzzy-neuron was designed for situation of many diagnoses. For multidimensional neo-fuzzy-neuron adaptive learning algorithms that are a modification of Widrow-Hoff algorithm were introduced. This system was approbate on nervous system diseases data set from University of California Irvine (UCI) Repository and show high level of classification results.enFair use conditionDla wszystkich w zakresie dozwolonego użytkumultidimensional neo-fuzzy-neuronMedical Data Miningcomputational intelligenceParkinson diseaselearning algorithmfuzzyficationwielowymiarowy neo-rozmyty-neuroneksploracja danych medycznychinteligencja obliczeniowachoroba Parkinsonaalgorytm uczeniarozmycieArtykułLUT LicenseLicencja PŁhttps://doi.org/10.34658/jacs.2017.1.39-4810.34658/jacs.2017.1.39-48