Comparing Association Rules and Deep Neural Networks on Medical Data

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dc.contributor.advisorOrdóñez, Carlos R.
dc.contributor.committeeMemberVilalta, Ricardo
dc.contributor.committeeMemberWu, Hulin
dc.creatorFund, Ian 1993-
dc.creator.orcid0000-0002-2483-5112
dc.date.accessioned2019-12-17T03:39:45Z
dc.date.available2019-12-17T03:39:45Z
dc.date.createdDecember 2019
dc.date.issued2019-12
dc.date.submittedDecember 2019
dc.date.updated2019-12-17T03:39:45Z
dc.description.abstractDeep neural networks are today's most popular tool for building predictive models across various different disciplines. A decade ago, the most popular predictive modeling technique was association rule mining. In this work, we carefully compare these two techniques in an effort to identify a more effective model with which to predict heart disease, a multi-prediction problem. Both techniques require significant knowledge, manual tuning, and experimentation to determine optimal parameters. Our goal was to build a predictive model that is at least as good as the best association rules across our entire data set. Promising results were obtained for some examples, while others still remain unclear. Making predictive models with medical data continues to be a challenging problem to solve that requires more attention from the scientific community.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/5558
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectNeural networks
dc.subjectNeurosciences
dc.subjectHeart disease
dc.subjectAssociation rules
dc.titleComparing Association Rules and Deep Neural Networks on Medical Data
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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