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dc.contributor.authorLópez, Nahúm Cueto
dc.contributor.authorGarcía-Ordás, María Teresa
dc.contributor.authorVitelli-Storelli, Facundo
dc.contributor.authorFernandez-Navarro, Pablo L 
dc.contributor.authorPalazuelos, Camilo
dc.contributor.authorAlaiz-Rodríguez, Rocío
dc.date.accessioned2022-05-05T12:20:06Z
dc.date.available2022-05-05T12:20:06Z
dc.date.issued2021-10-12
dc.identifier.citationInt J Environ Res Public Health. 2021 Oct 12;18(20):10670.es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/14285
dc.description.abstractThis study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.es_ES
dc.description.sponsorshipThe study was partially funded by the “Accion Transversal del Cancer”, approved on the Spanish Ministry Council on the 11th October 2007, by the Instituto de Salud Carlos III-FEDER (PI08/1770, PI08/0533, PI08/1359, PS09/00773, PS09/01286, PS09/01903, PS09/02078, PS09/01662, PI11/01403, PI11/01889, PI11/00226, PI11/01810, PI11/02213, PI12/00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150), by the Fundación Marqués de Valdecilla (API 10/09), by the ICGC International Cancer Genome Consortium CLL, by the Junta de Castilla y León (LE22A10-2), by the Consejería de Salud of the Junta de Andalucía (PI-0571), by the Conselleria de Sanitat of the Generalitat Valenciana (AP 061/10), by the Recercaixa (2010ACUP 00310), by the Regional Government of the Basque Country by European Commission grants FOOD-CT- 2006-036224- HIWATE, by the Spanish Association Against Cancer (AECC) Scientific Foundation, by the The Catalan Government DURSI grant 2009SGR1489. Samples: Biological samples were stored at the Parc de Salut MAR Biobank (MARBiobanc; Barcelona) which is supported by Instituto de Salud Carlos III FEDER (RD09/0076/00036). Furthermore, at the Public Health Laboratory from Gipuzkoa and the Basque Biobank. Furthermore, sample collection was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). Biological samples were stored at the “Biobanco La Fe” which is supported by Instituto de Salud Carlos III (RD 09 0076/00021) and FISABIO biobanking, which is supported by Instituto de Salud Carlos III (RD09 0076/00058).es_ES
dc.language.isoenges_ES
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI) es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBreast canceres_ES
dc.subjectFeature selectiones_ES
dc.subjectRisk prediction modeles_ES
dc.subjectStabilityes_ES
dc.titleEvaluation of Feature Selection Techniques for Breast Cancer Risk Predictiones_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID34682416es_ES
dc.format.volume18es_ES
dc.format.number20es_ES
dc.format.page10670es_ES
dc.identifier.doi10.3390/ijerph182010670es_ES
dc.contributor.funderGeneralitat Valenciana (España) es_ES
dc.contributor.funderRegional Government of Andalusia (España) es_ES
dc.contributor.funderJunta de Castilla y León (España) es_ES
dc.contributor.funderFundación La Caixa es_ES
dc.contributor.funderInstituto de Salud Carlos III es_ES
dc.contributor.funderGovernment of Catalonia (España) es_ES
dc.contributor.funderAsociación Española Contra el Cáncer es_ES
dc.contributor.funderUnión Europea. Comisión Europea es_ES
dc.contributor.funderBasque Government (España) es_ES
dc.contributor.funderFundación Marqués de Valdecilla es_ES
dc.contributor.funderInternational Cancer Genome Consortium es_ES
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) es_ES
dc.description.peerreviewedes_ES
dc.identifier.e-issn1660-4601es_ES
dc.relation.publisherversionttps://doi.org/10.3390/ijerph182010670es_ES
dc.identifier.journalInternational Journal of Environmental Research and Public Healthes_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FOOD-CT-2006-036224-HIWATEes_ES
dc.rights.accessRightsopen accesses_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICINN//RD09%2F0076%2F00054/ES/RED DE BIOBANCOS/ es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICINN//RD09%2F0076%2F00036/ES/RED DE BIOBANCOS/ es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICINN//RD09%2F0076%2F00021/ES/RED DE BIOBANCOS/ es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI08/1770es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI08/0533es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI08/1359es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PS09/00773es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PS09/01286es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PS09/01903es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PS09/02078es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PS09/01662es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI11/01403es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI11/01889es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI11/00226es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI11/01810es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI11/02213es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI12/00488es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI12/00265es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI12/00715es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI12/00150es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI12/01270es_ES


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