Colorectal Cancer Risk Prediction: A Machine Learning Approach
Principal Investigator
Name
Folusho Arokoyo
Degrees
Masters
Institution
University of Westminster
Position Title
Postgraduate Student
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1320
Initial CDAS Request Approval
Sep 5, 2023
Title
Colorectal Cancer Risk Prediction: A Machine Learning Approach
Summary
This study aims to evaluate the impact of feature selection on the performance of colorectal cancer risk prediction models using machine learning approaches. The study uses various data points, such as age, lifestyle factors and medical history to estimate the likelihood of individuals developing colorectal cancer in the future. Seven (7) different feature selection methods will be evaluated and the results will help to identify the most effective methods for CRC risk prediction. In general, this study is expected to make a significant contribution to the field of colorectal cancer research and help improve survival rates through earlier detection and treatment.
Aims
1. To evaluate the impact of feature selection on the performance of colorectal cancer risk prediction models.
2. To identify the key factors that are most relevant features for predicting colorectal cancer risk.
3. To gain insights into the underlying data that is used to predict colorectal cancer risk.
Collaborators
Habeeb Balogun