Predicting and characterizing survivability for breast cancer patients

dc.contributor.advisor Torres-García, Wandaliz López-González, Karen College of Engineering en_US
dc.contributor.committee Cabrera Rios, Mauricio
dc.contributor.committee Domenech García, Maribella
dc.contributor.department Department of Industrial Engineering en_US
dc.contributor.representative Zapata, Rocío 2019-04-15T12:45:47Z 2019-04-15T12:45:47Z 2018-12-12
dc.description.abstract The 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.description.graduationSemester Fall en_US
dc.description.graduationYear 2018 en_US
dc.language.iso en en_US
dc.rights.holder (c) 2018 Karen López González en_US
dc.rights.license All rights reserved en_US
dc.subject Breast cancer en_US
dc.subject data mining en_US
dc.subject prediction en_US
dc.subject genomic and clinical data en_US
dc.subject characterization en_US
dc.subject.lcsh Big data en_US
dc.subject.lcsh Data mining en_US
dc.subject.lcsh Survival analysis en_US
dc.subject.lcsh Breast -- Cancer -- Patients en_US
dc.subject.lcsh Machine learning en_US
dc.title Predicting and characterizing survivability for breast cancer patients en_US
dc.type Thesis en_US
dspace.entity.type Publication Industrial Engineering en_US M.S. en_US
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