College
Buchtel College of Arts and Sciences
Date of Last Revision
2025-05-06 06:37:48
Major
Statistics
Honors Course
STAT 498
Number of Credits
3
Degree Name
Bachelor of Science
Date of Expected Graduation
Spring 2025
Abstract
Heart disease remains the leading cause of death in the United States, particularly among the elderly population. The growing availability of large-scale health data and the advancement of machine learning tools present an opportunity to create more accurate and individualized predictive models. This study utilizes a subset of the 2020 Behavioral Risk Factor Surveillance System (BRFSS) dataset, focusing on individuals aged 70 and above, to explore predictive modeling using logistic regression, random forests, and XGBoost. The models were evaluated using key performance metrics, including sensitivity, specificity, accuracy, and the area under the ROC curve (AUC). The findings suggest that while logistic regression remains a strong baseline due to its interpretability, ensemble learning methods like the random forest and XGBoost show superior predictive performance, particularly in complex, high-dimensional settings. The paper is concluded by discussing the potential of integrating such models into public health screening and the broader implications.
Research Sponsor
Dr. Richard Einsporn
First Reader
Dr. Nao Mimoto
Second Reader
Dr. Mark Fridline
Honors Faculty Advisor
Dr. Nao Mimoto
Proprietary and/or Confidential Information
No
Recommended Citation
Cetin, Zeynep, "Predicting Heart Disease Using Machine Learning Models" (2025). Williams Honors College, Honors Research Projects. 2005.
https://ideaexchange.uakron.edu/honors_research_projects/2005