Data Preparation: Collect and preprocess the survival data, ensuring that it includes both the event times and censoring information. Model Training: Use software packages like R's 'randomForestSRC' or Python's 'scikit-survival' to train the RSF model. Variable Selection: Use variable importance measures to identify key factors influencing survival. Model Evaluation: Assess the model's performance using metrics like the concordance index (C-index).
