View Proposal

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Proposer

Eric Nimako Aidoo

Title

Classification of maternal health risk level during pregnancy: A comparison of machine learning approaches

Goal

Description

Maternal health during pregnancy remains one of the major public health concerns worldwide, particularly in low- and middle-income countries where the mortality rate is high. Early detection of maternal health risks during pregnancy is critically important for improving maternal healthcare and birth outcomes. Traditionally, maternal health risk assessment has relied on manual evaluation by clinical experts. However, with advancements in technology, machine learning models have emerged as valuable tools for supporting healthcare by identifying complex patterns within large datasets gathered from medical records. This study aims to develop and compare several machine learning models to classify maternal health risk levels during pregnancy. Additionally, it will explore the relationship between health risk levels and various clinical parameters among pregnant women to identify potential risk factors that can support early diagnosis and treatment.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Eric Nimako Aidoo

Keywords

machine learning models, maternal health, risk level, risk factors, health parameters

Degrees

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