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Risk Prediction Model for Endometrial Cancer using multiple Machine Learning Algorithms and Meta-Analysis

Principal Investigator
Name
Rebecca Karkia
Degrees
BMBS, MCh,
Institution
Brunel University
Position Title
Doctoral researcher
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-1636
Initial CDAS Request Approval
Aug 9, 2024
Title
Risk Prediction Model for Endometrial Cancer using multiple Machine Learning Algorithms and Meta-Analysis
Summary
I am doing my doctoral degree which involves validating an already in existence risk prediction model for endometrial cancer using neural networking. The initial data was requested under PLCO-590. I would like to request access to same dataset which contains the risk factors associated with this type of cancer, preferably, BMI, SmokingStatus, Age, Parity, Breastfeeding, HRT use, Type 2 Diabetes, Hypertension, Contraceptive Use and the diagnosis.
Aims

To validate an already in existence neural networking algorithm which classifies endometrial cancer risk
The testing dataset is an even larger population dataset known as the CPRD dataset.

Collaborators

Emmanouil Karteris, Brunel University