Machine Learning for Colorectal Cancer Risk Prediction

Principal Investigator

Name
Ling Zheng

Degrees
Ph.D.

Institution
Monmouth University

Position Title
Assistant Professor

Email
lzheng@monmouth.edu

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-687

Initial CDAS Request Approval
Nov 17, 2020

Title
Machine Learning for Colorectal Cancer Risk Prediction

Summary
Colorectal cancer (CRC) is the third most prevalent cancer and the second most common cause of cancer deaths in the United States. Colorectal cancer screening is one of the most powerful tools for prevention. Unfortunately, about 1 in 3 people in the US who should get tested for colorectal cancer have never been screened. This is a thesis project for a Software Engineering Master student at Monmouth University. The goal is to design a machine learning-based approach for predicting populations’ CRC risk and identifying populations with a higher risk at an early stage for prevention. Public CRC datasets, e.g., the National Health Interview Survey (NHIS) dataset and the longitudinal Prostate, Lung, Colorectal, Ovarian (PLCO) Cancer Screening dataset from the National Cancer Institute, will be explored and utilized for the study.

Aims

1. Compare performances of different machine learning algorithms for the colorectal cancer risk prediction
2. Identify highly related risk factors of colorectal cancer

Collaborators

Elijah O. Eniola, Monmouth University