This is repository of the "Enhancing Survival Analysis Model Selection Through XAI(t) in Healthcare" paper.

The code is written in R and Python and it is composed by two main parts:

1. Preprocessing part: Data Reading and formatting, dropping of non relevant features, categorization, statistical analysis and feature selection.
2. Analysis part: Survival Curves, Models Training and Validation, XAI(t).

The python libraries requested are:

• pandas, torch, pycox, reticulate, numpy, seaborn, scipy, statsmodels, scikit-learn

The R libraries requested are:

• survival, surviminer, survex, randomForestSRC, gbm, survivalsvm, ggsurvfit, ggplot2, pec, caret, SurvMetrics, mlr3proba, mlr3extralearners, mlr3pipelines, mlr3tuning, survivalmodels, reticulate

For running CoxTime model it is suggested to eventually install the following librareis: pip install numpy==1.23.5 pip install pandas==1.5.3 pip install numba==0.56.4