Predicting cancer survival at different stages: Insights from fair and explainable machine learning approaches

Abstract

Objectives: While prior machine learning (ML) models for cancer survivability prediction often treated all cancer stages uniformly, cancer survivability prediction should involve understanding how different stages impact the outcomes. Additionally, the success of ML-powered cancer survival prediction models depends a lot on being fair and easy to understand, especially for different stages of cancer. This study addresses cancer survivability prediction using fair and explainable ML methods.

Methods: Focusing on bladder, breast, and prostate cancers using SEER Program data, we developed and validated fair and explainable ML strategies to train separate models for each stage. These computational strategies also advance the fairness and explainability of the ML models.

Results: The current work highlights the important role of ML fairness and explainability in stage-specific cancer survivability prediction, capturing and interpreting the associated factors influencing cancer survivability.

Conclusions: This contribution advocates for integrating fairness and explainability in these ML models to ensure equitable, fair, interpretable, and transparent predictions, ultimately enhancing patient care and shared decision-making in cancer treatment.

Keywords: Cancer survivability; Machine learning explainability; Machine learning fairness.