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dc.contributor.advisorTorres-García, Wandaliz
dc.contributor.authorLópez-González, Karen
dc.description.abstractThe negative impact surrounding breast cancer as a disease affecting mainly women has caught the attention of practitioners and researchers around the world. Data mining techniques and health care have been integrated to improve breast cancer prediction in patients. Nonetheless, these models could be improved further by exploring in more detail their structure. Hence, this work's objective is to improve disease's comprehension by modeling survivability's behavior on breast cancer patients and extracting molecular patterns that characterize those with higher risk of dying. The implementation of a multi-step data mining approach for the construction of these models includes an in-depth biological interpretation of important variables.en_US
dc.subjectBreast canceren_US
dc.subjectdata miningen_US
dc.subjectgenomic and clinical dataen_US
dc.subject.lcshBig dataen_US
dc.subject.lcshData miningen_US
dc.subject.lcshSurvival analysisen_US
dc.subject.lcshBreast -- Cancer -- Patientsen_US
dc.subject.lcshMachine learningen_US
dc.titlePredicting and characterizing survivability for breast cancer patientsen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c) 2018 Karen López Gonzálezen_US
dc.contributor.committeeCabrera Rios, Mauricio
dc.contributor.committeeDomenech García, Maribella
dc.contributor.representativeZapata, Rocío Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Industrial Engineeringen_US

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    Items included under this collection are theses, dissertations, and project reports submitted as a requirement for completing a graduate degree at UPR-Mayagüez.

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