View Proposal

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Proposer

Eric Nimako Aidoo

Title

A comparison of machine learning models for breast cancer risk classification

Goal

Description

Breast cancer remains one of the most common cancers among women, affecting millions of individuals each year. Timely identification of breast cancer is crucial for improving patient outcomes and reducing mortality rates. Traditionally, breast cancer diagnosis has relied on manual evaluation by medical professionals, including physical examinations, imaging techniques, and biopsy analysis. However, with the advent of advanced technologies, machine learning models have emerged as powerful tools in healthcare, enabling the identification of complex patterns within vast datasets derived from medical records, imaging scans, and genetic profiles. This study aims to develop and compare various machine learning models to classify breast cancer risk levels and enhance diagnostic precision. Furthermore, it will investigate the relationship between risk levels and potential risk factors to support early detection and targeted treatment strategies.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Eric Nimako Aidoo

Keywords

machine learning models, breast cancer risk, risk level classification, risk factors.

Degrees

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