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dc.contributor.authorSolanki, Yogendra Singh
dc.contributor.authorChakrabarti, Prasun
dc.contributor.authorJasiński, Michał
dc.contributor.authorLeonowicz, Zbigniew
dc.contributor.authorBolshev, Vadim
dc.contributor.authorVinogradov, Alexander
dc.contributor.authorJasińska, Elżbieta
dc.contributor.authorGoňo, Radomír
dc.contributor.authorNami, Mohammad
dc.date.accessioned2021-06-08T06:24:48Z
dc.date.available2021-06-08T06:24:48Z
dc.date.issued2021
dc.identifier.citationElectronics. 2021, vol. 10, issue 6, art. no. 699.cs
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10084/143078
dc.description.abstractNowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier's performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesElectronicscs
dc.relation.urihttps://doi.org/10.3390/electronics10060699cs
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectbreast cancer prognosiscs
dc.subjectsupervised machine learning classifiercs
dc.subjectdata selectioncs
dc.subjectimbalance handlingcs
dc.titleA hybrid supervised machine learning classifier system for breast cancer prognosis using feature selection and data imbalance handling approachescs
dc.typearticlecs
dc.identifier.doi10.3390/electronics10060699
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume10cs
dc.description.issue6cs
dc.description.firstpageart. no. 699cs
dc.identifier.wos000634365700001


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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.