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Application of machine learning methods for the prediction of distress in patients with oncological diseases

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dc.creator Marinova, Ginka Kaleva
dc.creator Ganchev, Todor
dc.creator Nikolov, Nedyalko
dc.date.accessioned 2021-02-11T12:26:24Z
dc.date.available 2021-02-11T12:26:24Z
dc.date.issued 2020-12-31
dc.identifier https://aj-tuv.org/index.php/ajtuv/article/view/204
dc.identifier info:doi/10.29114/ajtuv.vol4.iss2.204
dc.identifier urn:ISSN:2603-316X
dc.identifier.uri http://dspace.tu-varna.bg/handle/123456789/137
dc.description Distress management is of particular importance in all disease treatment strategies that aim to cope with medical conditions, which require prolonged therapy. Here, we present results obtained in a comparative study of various classification methods for automated distress detection. For the purposes of the present study, use was made of a common experimental protocol that relies on a dataset of approximately 6 000 oncological patients at different stages of therapy. The dataset consists of the binary responses to specific questions in a purposefully-designed self-evaluation questionnaire on the degree of distress. Conducted, within such a framework, was a performance assessment of three distress detectors based on Multilayer Perceptron Neural Network (MLP NN), boosting and bagging meta-classification methods and evaluated, further, was the performance of nine characteristic descriptors (KR1-KR9) representing the informative content of the dataset in different ways. The results obtained in the experiments prove conclusively that one of the characteristic descriptors, KR8 and KR9, significantly outperform the other descriptors in terms of classification accuracy, precision, recall, and F-measure.
dc.format application/pdf
dc.language eng
dc.publisher Technical university of Varna, Bulgaria
dc.relation https://aj-tuv.org/index.php/ajtuv/article/view/204/65
dc.rights Copyright (c) 2021 Ginka Kaleva Marinova, Todor Ganchev, Nadyalko Nikolov
dc.rights http://creativecommons.org/licenses/by/4.0
dc.source ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA; Vol 4 No 2 (2020): Annual Journal of Technical University of Varna; 130-137
dc.source ГОДИШНИК НА ТЕХНИЧЕСКИ УНИВЕРСИТЕТ - ВАРНА; Vol 4 No 2 (2020): Annual Journal of Technical University of Varna; 130-137
dc.source ЕЖЕГОДНЫЙ ЖУРНАЛ ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА ВАРНЫ, БОЛГАРИЯ; Vol 4 No 2 (2020): Annual Journal of Technical University of Varna; 130-137
dc.title Application of machine learning methods for the prediction of distress in patients with oncological diseases
dc.type info:eu-repo/semantics/article
dc.type info:eu-repo/semantics/publishedVersion


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