View Proposal
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Proposer
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Eric Nimako Aidoo
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Title
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Machine Learning Applications in Disease Modelling
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Goal
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Develop machine learning models to describe the impact of climate change on the incidence and mortality of noncommunicable diseases
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Description
- Noncommunicable diseases such as cancer, diabetes, respiratory diseases, and cardiovascular diseases remain major challenges in global health management. Despite advancements in healthcare delivery and accessibility, it is estimated that 41 million people die annually due to noncommunicable diseases.
Among several factors, climate change is increasingly recognized as a significant factor influencing the incidence and mortality of noncommunicable diseases. For instance, existing studies have shown that extreme temperatures impact cardiovascular diseases, while air pollution affects respiratory diseases.
The need for effective and efficient machine learning ensembles to uncover complex patterns and relationships in such data has become important.
- Resources
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Background
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Url
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Difficulty Level
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Moderate
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Ethical Approval
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None
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Number Of Students
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3
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Supervisor
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Eric Nimako Aidoo
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Keywords
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machine learning models, noncommunicable diseases incidence, model comparison
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Degrees
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Bachelor of Science in Computer Science
Master of Science in Data Science
Master of Science in Information Technology (Business)
Bachelor of Science in Statistical Data Science