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

作者：Reuben Adatorwovor, Motolani E. Ogunsanya, Bin Huang, Richard Charnigo, Olufunmilola Abraham · 发表于：JMIR Cancer · 年份：2025 · DOI：10.2196/72665 · 被引用次数：4 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Screening and Detection

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 analyses aim to improve risk stratification, treatment planning, and prognosis for overall colon cancer survival within subgroups. Methods: We conducted a retrospective analysis of colon cancer cases diagnosed between 2010 and 2022 (n=33,825), using Kentucky Cancer Registry data linked to mortality records, with approval from the University of Kentucky Institutional Review Board (#63067). We compared multiple predictive modeling techniques, including Cox proportional hazards, accelerated failure time models, Extreme Gradient Boosting, random survival forests, least absolute shrinkage and selection operator (LASSO), and elastic net regression, to estimate survival probabilities. The Kaplan-Meier method provided ...