This study uses different supervised machine learning techniques to build predictive models to predict the 10-year risk of coronary heart disease. It also compares, identifies, and selects the model with the least test error rate and the highest prediction accuracy.
Faculty Mentor: Dr. Rebecca Pierce
Department of Mathematical Sciences
Graduate