Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach.

The aim of our study was to assess the overall survival rates for colorectal cancer at 3 years and to identify associated strong prognostic factors among patients in Morocco through an interpretable machine learning approach. This approach is based on a fully non-parametric survival random forest (RSF), incorporating variable importance and partial dependence effects. The data was povided from a retrospective study of 343 patients diagnosed and followed at Hassan II University Hospital.

Risk factors affecting patients survival with colorectal cancer in Morocco : Survival Analysis using an Interpretable Machine Learning Approach.

The aim of our study was to assess the overall survival rates for colorectal patients in Morocco and to identify strong prognostic factors using a novel approach combining survival random forest and the Cox model. Covariate selection was performed using the variable importance based on permutation and partial dependence plots were displayed to explore in depth the relationship between the estimated partial effect of a given predictor and survival rates. The predictive performance was measured by two metrics, the Concordance Index (C-index) and the Brier Score (BS).