Spring 2024 Graduate Student Thesis Defense

Friday, April 5, 2024
2:00 PM - 3:00 PM
WH room 286

Predicting Heart Disease Based on Patient Characteristics

Speaker: Lizzy Eccles

Abstract: Heart disease is a major concern worldwide. Everyone must know the importance of early detection and intervention when it comes to this life-threatening disease. In this research paper, multiple unsu- pervised and supervised learning techniques are employed to predict the likelihood of heart disease based on many factors. These factors range from clinical features to demographic characteristics. A logis- tic regression algorithm, a random forest algorithm, and a decision tree are applied to a data set to construct the most applicable predic- tive model. The overall analysis resulted in a random forest model with 98.88% accuracy along with many other interesting findings.

Thesis Advisor: Dr. Mezbahur Rahman