View Proposal

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Proposer

Eric Nimako Aidoo

Title

Bayesian modelling of maternal health risk level during pregnancies

Goal

Description

Maternal health during pregnancy continues to be a significant public health issue across the globe, particularly in low- and middle-income countries where maternal mortality rates remain alarmingly high. Identifying health risks in pregnant women at an early stage is essential for timely interventions that enhance both maternal and neonatal health outcomes. Traditional methods of risk assessment often rely on clinical expertise and fixed scoring systems, which may not fully account for the inherent complexity and uncertainty within patient data. Bayesian modelling presents a compelling alternative by integrating prior clinical knowledge with observed data and explicitly quantifying uncertainty in risk predictions. This study aims to utilise Bayesian modeling techniques to predict maternal health risk levels during pregnancy and examine the relationships between these risk levels and potential risk factors that can support early diagnosis and targeted interventions, ultimately contributing to more effective maternal healthcare strategies.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Eric Nimako Aidoo

Keywords

bayesian 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