Abstract
The medical data and its classification should be particularly treated. The data can not be modified or altered, because this could lead to overestimation or false decisions. Some classifiers, using random factors, can generate false, higher overall accuracy of diagnosis. Medical support systems should be trustworthy and reliable even at the cost of system complexity. In this paper fusion of two classifiers has been proposed, where k–NN classifier and classifier based on a justified granulation paradigm were employed. Additionally, proposed solution allows to visualize obtained classification results. Accuracy of the proposed solution has been compared with various classifiers. All methods presented in this work were tested on real medical data coming from three medical datasets. Finally, some remarks for further research have been proposed.
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Bernas, M., Orczyk, T., Porwik, P. (2015). Fusion of Granular Computing and \(k\)–NN Classifiers for Medical Data Support System. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_7
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DOI: https://doi.org/10.1007/978-3-319-15705-4_7
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