Stage-specific cancer survival prediction enriched by explainable machine learning

TL;DR

This study develops and verifies explainable machine learning models for stage-specific cancer survival prediction, revealing stage-dependent feature influences and enhancing transparency for personalized treatment planning.

Contribution

It introduces stage-specific survival prediction models using explainability techniques, addressing limitations of traditional combined-stage models and providing clinical insights.

Findings

Stage-specific models outperform traditional models in accuracy.

SHAP and LIME reveal stage-dependent feature importance.

Insights support personalized treatment strategies.

Abstract

Despite the fact that cancer survivability rates vary greatly between stages, traditional survival prediction models have frequently been trained and assessed using examples from all combined phases of the disease. This method may result in an overestimation of performance and ignore the stage-specific variations. Using the SEER dataset, we created and verified explainable machine learning (ML) models to predict stage-specific cancer survivability in colorectal, stomach, and liver cancers. ML-based cancer survival analysis has been a long-standing topic in the literature; however, studies involving the explainability and transparency of ML survivability models are limited. Our use of explainability techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enabled us to illustrate significant feature-cancer stage interactions that would have remained hidden in traditional black-box models. We identified how certain demographic and clinical variables influenced survival differently across cancer stages and types. These insights provide not only transparency but also clinical relevance, supporting personalized treatment planning. By focusing on stage-specific models, this study provides new insights into the most important factors at each stage of cancer, offering transparency and potential clinical relevance to support personalized treatment planning.