An Evaluation of Machine Learning Algorithms for Cardiovascular Disease Detection
Description
This thesis aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every 33 seconds in the United States alone. As the leading cause of death, early identification becomes critical for early intervention and prevention. The study addresses key research questions, including the role of machine learning in enhancing heart disease detection, comparative analysis of the six machine learning models, and the importance of predictive indicators. By leveraging machine learning algorithms for medical data interpretation, the thesis contributes insights into early disease detection.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024-05
Agent
- Author (aut): La, Nikki
- Thesis director: Sheehan, Connor
- Committee member: Connor, Dylan
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): School of Mathematical and Statistical Sciences