Lin, YingWang, YapingValier, HelenOdo, Chiwetalu P.2021-09-162021-09-162020-12https://hdl.handle.net/10657/8261Heart failure (HF) is a global pandemic affecting more than 26 million patients worldwide. Effective management of risk factors is extremely important for reducing heart failure. Lifestyle modification can effectively reduce the risk of heart failure but clinical guidelines are generalized and not tailored for individuals. This project developed a rule-based framework that automatically generates personalized lifestyle modification recommendations for heart failure risk reduction. The proposed framework integrates an ensemble learning-based rule discovery model (RuleFit) that translates the patient-level profiles into actionable patterns (rules), and a rule-based optimization algorithm that searches for the optimal lifestyle modification recommendations based on the patient’s unchangeable profile. The proposed framework was applied to a large population in the Atherosclerosis Risk in Communities (ARIC) study to manage patient risk of fatal coronary heart disease events.enThe 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).Individualized lifestyle recommendationsExpert systemsPatient-centered healthcareSupervised machine learningRuleFit ensembleNetwork-assisted optimizationOperations researchHonors Thesis