Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

dc.contributor.authorFlorensa Cazorla, Dídac
dc.contributor.authorMateo Fornés, Jordi
dc.contributor.authorSolsona Tehàs, Francesc
dc.contributor.authorPedrol, Tere
dc.contributor.authorMesas, Miquel
dc.contributor.authorPiñol, Ramon
dc.contributor.authorGodoy i García, Pere
dc.date.accessioned2022-10-14T11:18:53Z
dc.date.available2022-10-14T11:18:53Z
dc.date.issued2022
dc.description.abstractBackground: Previous works have shown that risk factors are associated with an increased likelihood of colorectal cancer. Objective: The purpose of this study was to detect these associations in the region of Lleida (Catalonia) by using multiple correspondence analysis (MCA) and k-means. Methods: This cross-sectional study was made up of 1083 colorectal cancer episodes between 2012 and 2015, extracted from the population-based cancer registry for the province of Lleida (Spain), the Primary Care Centers database, and the Catalan Health Service Register. The data set included risk factors such as smoking and BMI as well as sociodemographic information and tumor details. The relations between the risk factors and patient characteristics were identified using MCA and k-means. Results: The combination of these techniques helps to detect clusters of patients with similar risk factors. Risk of death is associated with being elderly and obesity or being overweight. Stage III cancer is associated with people aged ≥65 years and rural/semiurban populations, while younger people were associated with stage 0. Conclusions: MCA and k-means were significantly useful for detecting associations between risk factors and patient characteristics. These techniques have proven to be effective tools for analyzing the incidence of some factors in colorectal cancer. The outcomes obtained help corroborate suspected trends and stimulate the use of these techniques for finding the association of risk factors with the incidence of other cancers.ca_ES
dc.description.sponsorshipThis work was supported by contract 2019-DI-43 from the Industrial Doctorate Program of the Government of Catalonia and by the Spanish Ministry of Science and Innovation under contract PID2020-113614RB-C22. Some of the authors are members of the research group 2014-SGR163, funded by the Generalitat de Catalunya.ca_ES
dc.identifier.doihttps://doi.org/10.2196/29056
dc.identifier.idgrec032583
dc.identifier.issn1438-8871
dc.identifier.urihttp://hdl.handle.net/10459.1/83940
dc.language.isoengca_ES
dc.publisherJMIR Publicationsca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113614RB-C22/ES/COMPUTACION AVANZADA PARA LOS RETOS DE LA SOCIEDAD DIGITAL/ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.2196/29056ca_ES
dc.relation.ispartofJournal of Medical Internet Research, 2022, vol. 24, núm. 7, e29056ca_ES
dc.rightscc-by (c) Florensa et al., 2022ca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectColorectal cancerca_ES
dc.subjectCancer registryca_ES
dc.subjectMultiple correspondence analysisca_ES
dc.subjectk-meansca_ES
dc.subjectRisk factorsca_ES
dc.titleUse of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Studyca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
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