Ordóñez, Carlos R.2019-12-172019-12-17December 22019-12December 2https://hdl.handle.net/10657/5558Deep 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.application/pdfengThe 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).Neural networksNeurosciencesHeart diseaseAssociation rules2019-12-17Thesisborn digital