Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods

TL;DR

This study demonstrates that machine learning models for colorectal cancer survivability prediction perform better when tailored to specific ethnic groups and highlights the importance of ethnicity in personalized cancer prognosis.

Contribution

It introduces ethnicity-specific models for colorectal cancer survivability prediction and shows their improved performance over generic models using the SEER database.

Findings

Models perform better on single-ethnicity populations.

Feature importance varies across ethnic groups.

Achieved higher AUC scores than previous studies.

Abstract

In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient populations. Our models consistently perform better on single-ethnicity populations and provide different feature importance rankings when trained in different populations. Additionally, we show our models achieve higher Area Under Curve (AUC) score than the best reported in the literature. We also apply imbalanced classification techniques to improve classification performance when the number of patients who have survived from colorectal cancer is much larger than who have not. These results provide evidence in favor for increased consideration of patient ethnicity in cancer survivability prediction, and for more personalized medicine in general.