Full metadata
Title
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
2024-05
Contributors
- La, Nikki (Author)
- Sheehan, Connor (Thesis director)
- Connor, Dylan (Committee member)
- Barrett, The Honors College (Contributor)
- School of Mathematical and Statistical Sciences (Contributor)
Topical Subject
Resource Type
Extent
44 pages
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2023-2024
Handle
https://hdl.handle.net/2286/R.2.N.191839
System Created
- 2024-03-21 12:39:58
System Modified
- 2024-03-28 11:38:23
- 7 months 1 week ago
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