Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach.

ABSTRACT:

Background

Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and nonlinear relationships between risk factors. Recently, machine learning (ML) techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns.

Objective

This study compares several ML models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry. By identifying key risk factors, these analyse

SUBMITTER: Adatorwovor R 

PROVIDER: S-EPMC12655889 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature