# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
## 5 Conclusions
### Pipeline Workflow Pseudo-Code  
```
1:Full Dataset \(\leftarrow\) Data Loading and Pre-processing
2:training_set, test_set \(\leftarrow\) Train_test_split(Full Dataset, Stratified_by = event, Ratio = 0.7)
3:features \(\leftarrow\) feature_selection(training_set)
4:training_set \(\leftarrow\) training_set(features)
5:test_set \(\leftarrow\) test_set(features)
6:performance_list \(\leftarrow\) list()
7:Model List \(\leftarrow\) (CPH, SRF, SSVM, SGBM, CT, DH, DS, LH, PCH)
8:Task \(\leftarrow\) SurvTask(time, event, training_ set)
9:
10:formodel in Model Listdo
11:ifmodel \(==(CPH||SRF)\)then
12:\(model.fit(time,event)\sim training\_set(features)\)
13:else
14:composite_learner \(\leftarrow\) mlr3_learner(model, parameters)
15:\(model\gets composite\_learner.fit(Task)\)
16:endif
17:ExplainerTr \(\leftarrow\) create_explainer(model, \(training\_set,target=training(time,event)\))
18:ExplainerTest \(\leftarrow\) create_explainer(model, \(test\_set,target=test(time,event)\))
19:test_metrics \(\leftarrow\)model_performance(ExplainerTest)
20:perfomance_list \(\leftarrow\) append(test_metrics)
21:endfor
22:
23:best_models \(\leftarrow\) select_model(performance_list)
24:Model SurvSHAP \(\leftarrow\) SurvSHAP(ExplainerTr(best_models), new_observations=test_set)
25:Feature importance \(\leftarrow\) model_parts(ExplainerTest(best_models))
```  
**Algorithm 1** Pipeline Pseudo-Code  
+++ ==WARNING: Truncated because of repetitions==
71)  
++  
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