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Predicting Endometriosis Using Machine Learning

Principal Investigator
Name
Jessica Duffield
Degrees
M.Sc.
Institution
University of the West of England
Position Title
Student
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-928
Initial CDAS Request Approval
Mar 2, 2022
Title
Predicting Endometriosis Using Machine Learning
Summary
For my Master's Thesis in Data Science, I would like to create a machine learning model to predict the probability that a patient has endometriosis given other factors about their health and family history. Not much research has been done on the matter so far in literature, and currently the main way to diagnose the condition is through invasive procedures such as surgery. Creating a machine learning model such as the one proposed would be able to speed up diagnosis for a patient from years to much less, and could save health services money when surgery may not be required. The data includes if a patient has endometriosis or not and lots of other information regarding their health so would be very useful to me in my proposed project.

As stated, this project is to be completed in relation to my Master's thesis in Data Science at the University of the West of England (UK).
Aims

- Create a machine learning model to predict how likely a patient is to have endometriosis
- Investigate the relationship between endometriosis and a patient's health and family history

Collaborators

None