Abstract
In this paper, we propose two methods of application of federated learning to the construction of classifiers for the analysis of data related to predicting the death of patients suffering from the vasculitis. The paper contains results of experiments on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland. In order to evaluate the proposed methods, which are trained on data samples, we compared their functionality with the work results of classical classifiers trained on the entire data. It turned out that the quality of classification of federated learning methods is comparable to the quality of classical methods. This means that access to the whole data is not necessary to construct effective classifiers for the considered decision problem.
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This paper was partially supported by the Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge of University of Rzeszów, Poland.
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Bazan, J.G., Milan, P., Bazan-Socha, S., Wójcik, K. (2023). Application of Federated Learning to Prediction of Patient Mortality in Vasculitis Disease. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_36
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