# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
## 4 Discussion
### Explanation Methods  
The model explanation methods can be classified into two categories: dataset-level explanations and model-level explanations. The former category aims at analyzing the dataset characteristics to understand their impact on event predictions; the latter focuses on ranking and highlighting the features that the model considers important for predictions. Intuitively, both methods present some limitations: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Year** & **Task** & **Input Data** & **Survival Analysis Model** & **Explainability Model** \\ \hline [PERSON] et al. [13] & 2023 & Prognosis of DLBCL & Transcriptomic data & AutoEncoders & DeepSHAP \\ \hline [PERSON] et al. [21] & 2023 & Prognosis of NPC & CT images, clinical data & Linear Regression, KNN, support vector machines, Naive Bayes, tree-based models, \\ \hline [PERSON] et al. [22] & 2023 & Prognosis of pancreatic cancer & CT images, clinical data & Convolutional Neural Networks, support vector machines & SHAP, LIME \\ \hline [PERSON] et al. [23] & 2023 & Prediction of BMT efficacy & Clinical data & Tree-base models, Linear Regression, KNN, AdaBoost, CartBoost \\ \hline [PERSON] et al. [24] & 2021 & Prognosis of hepatitis & Clinical and demographic data & Linear Regression, CART, KNN, leave Bayes \\ \hline [PERSON] et al. [50] & 2023 & Prognosis of RCC & Genomic data & LASSO-Cox & SHAP, LIME \\ \hline [PERSON] et al. [51] & 2023 & Identification of EGFR in lung adenocarcinoma & CT images & Light GBM, Linear Regression, tree-based models & SHAP, LIME \\ \hline [PERSON] et al. [12] & 2024 & Prognosis of breast cancer & Clinical and demographic data & Cox Mixtures, DeepSurv, Cox PH, survival random forest \\ \hline [PERSON] et al. [19] & 2023 & Prediction of Text data, tabular data, hospital LoS & Tree-based models, CoxPH, LergeSurv, DeepFit & SurvSHAP, SurvLIME \\ \hline [PERSON] et al. [20] & 2023 & Test XAI on SA for BMT & Clinical and demographic data & CoxPH, survival random forest & SurvSHAP, SurvLIME \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Related works. * Dataset-level explanation limitations: SHAP is designed to return a local explanation, i.e., it gives an explanation for a single sample. Consequently, SurvSHAP behaves in the same way. When used on a dataset, its resulting explanation depends on the sample distribution. In fact, if the data are unbalanced for specific features, their contribution will be minimal, but this conclusion cannot be generalized to other data. Hence, the ideal scenario could be to use a large dataset for the sake of higher generalization of the results. However, the computation of feature contributions for a single sample is computationally expensive because of model complexity and the operations involved (e.g., multiple feature permutations, predictions, and performance computations for SHAP values; local sample generation, local model training, and prediction for LIME values). In XAI(t), this is exacerbated since the feature contributions are also computed for different time instants. * Model-level explanation limitations: Although model-level explanations are computationally less expensive than dataset-level ones, they return explanation information at the population level that cannot be used for explanations at a single prediction level. Moreover, the explanation methods based on permutation can lead to misinterpretations when the independent variables are strongly correlated [52]. To overcome the limitations of both explanation methods, they can be exploited in a complementary way: while the model-level explanation method can be used to identify the most important features affecting predictions, the dataset-level explanation method can be used to investigate how they affect predictions.