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Cancer Prevention

Cancer Prevention

Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World

Chengkun Sun¹, Erin Mobley^2,3, Michael Quillen⁴

¹Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 1889 Museum Road, Office 7020, Gainesville, FL, 32611, United States, 1 3526279467.

|June 19, 2025

Summary

This summary is machine-generated.

Machine learning models can predict early-onset colorectal cancer (EOCRC) in individuals under 45 using electronic health records. This research identifies key risk factors, aiding in earlier diagnosis and prevention strategies for young adults.

Keywords:

SHAP Americans CRC EHR ML Shapley Additive Explanations United States adolescent colorectal cancer diagnosis electronic health record machine learning middle-aged prediction prevention and treatment rectal cancer youth

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Area of Science:

Oncology
Data Science
Public Health

Background:

Colorectal cancer is the leading cause of cancer death in young Americans.
Early prediction and understanding risk factors for early-onset colorectal cancer (EOCRC) are crucial for patients under the recommended screening age.

Purpose of the Study:

To predict EOCRC in individuals under 45 using machine learning (ML) and electronic health record (EHR) data.
To explore potential risk and protective factors for early diagnosis of EOCRC.

Main Methods:

Developed separate ML models for colon cancer (CC) and rectal cancer (RC) using EHR data from patients under 45.
Assessed prediction across multiple time windows (0-5 years) using various ML algorithms and propensity score matching.
Interpreted models using Shapley Additive Explanations to identify key risk factors.

Main Results:

Models achieved AUC scores up to 0.811 for CC and 0.829 for RC prediction.
Key predictive features included immune/digestive disorders, secondary malignancies, and underweight status; blood diseases were specific to CC.
Propensity score matching ensured robustness against confounding variables.

Conclusions:

ML models using EHR data show potential for early EOCRC prediction in individuals under 45.
Identified significant risk factors can inform earlier detection and prevention strategies.
This study offers preliminary insights for improving EOCRC outcomes in younger populations.