Comparison of bioinspired algorithms applied to cancer database

dc.creatorSilva, Jesús
dc.creatorVillareal-González, Reynaldo
dc.creatorVarela, Noel
dc.creatorMaco, José
dc.creatorVillón, Martín
dc.creatorMarín–González, Freddy
dc.creatorPineda Lezama, Omar Bonerge
dc.date2021-01-15T18:14:09Z
dc.date2021-01-15T18:14:09Z
dc.date2021
dc.date.accessioned2023-10-03T19:02:10Z
dc.date.available2023-10-03T19:02:10Z
dc.identifierhttps://hdl.handle.net/11323/7700
dc.identifierhttps://doi.org/10.1007/978-981-15-7234-0_87
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9166983
dc.descriptionCancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languagespa
dc.publisherCorporación Universidad de la Costa
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceAdvances in Intelligent Systems and Computing
dc.sourcehttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_87
dc.subjectBig data
dc.subjectMachine learning
dc.subjectCancer prediction
dc.titleComparison of bioinspired algorithms applied to cancer database
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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