View Proposal

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Proposer

Eric Nimako Aidoo

Title

Classification of cardiovascular disease risk: A comparison of machine learning approaches

Goal

Description

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality worldwide, affecting millions of individuals annually. Early detection of CVD is essential for improving clinical outcomes and reducing the burden on healthcare systems. Traditionally, diagnosis and risk assessment for cardiovascular conditions have relied on clinical evaluation, medical imaging, blood tests, and patient history. However, the rise of advanced technologies has paved the way for the integration of machine learning in healthcare, offering powerful capabilities to uncover complex patterns within large-scale datasets, including electronic health records, imaging data, and genetic information. This study aims to develop and compare various machine learning models to classify cardiovascular disease risk levels and improve diagnostic accuracy. Additionally, it will explore the associations between identified risk levels and contributing factors, supporting early intervention and personalized treatment strategies.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Eric Nimako Aidoo

Keywords

machine learning models, cardiovascular disease, risk level classification, risk factors.

Degrees

Master of Science in Data Science
Bachelor of Science in Statistical Data Science
BSc Data Sciences