Open Access Honors Program Thesis
Marius Somodi, Honors Thesis Advisor, Department of Mathematics
Coronary heart disease--Risk factors; Forecasting--Mathematical models;
Coronary heart disease has long been a key area of focus in the discussion of public health. As such, numerous studies have been conducted throughout history with the sole intention of identifying risk factors leading to the onset of cardiovascular conditions. A plethora of statistical procedures can be used to identify an individual’s risk of developing heart disease, yet regression models tend to be the default tool used by researchers. Using the data obtained from the most influential cardiovascular study to date, the Framingham Heart Study, this analysis uses machine learning techniques to generate and test the predictive power of four different classification methods: logistic regression models, decision trees, random forests, and support vector machines. The findings of this study indicate that logistic regression is the most accurate classification technique; it correctly predicts whether an individual will develop coronary heart disease more than 84% of the time.
Year of Submission
Department of Mathematics
University Honors Designation
A thesis submitted in partial fulfillment of the requirements for the designation University Honors
1 PDF file (31 pages)
©2020 Jack Scott Glienke
Glienke, Jack Scott, "Life and death: Quantifying the risk of heart disease with machine learning" (2020). Honors Program Theses. 415.