## 5 Interpretation of Results with SHAP  
A game theoretic technique called SHAP can be used to explain the output of any ML model. Estimating the contribution of each feature in a dataset to the prediction made by the model is possible with feature importance. It is possible to determine which features have the greatest influence on a model's decision making by executing feature importance tests. This allows us to eliminate features that have little bearing on the model's predictions and concentrate on enhancing the more important features. In this research, SHAP was used to explain the model both globally and locally. Figure 6 shows the 20 most important features that influence the prediction process. This list shows the most important features in descending order. The SHAP value is shown on the x-axis of the graph, while all the features are shown on the y-axis. One SHAP value for a prediction and a feature is represented by each point on the graph. Red indicates higher feature values, whereas blue denotes lower feature values. The directionality influence of the characteristics can be generalized based on the distribution of the red and blue dots. The features selected by SHAP matched the features selected by the FPA. Eleven features were common in both of the processes. The common features selected by the FPA and SHAP are _blood sodium_, _heart rate_, _age_, _blood calcium_, _renal failure_, _platelets_, _urea nitrogen_, _MCV_, _creatinine_, _chloride_, _and BMI_. Figure 6 shows that higher lymphocyte values increase the likelihood of survival, while lower lymphocyte values increase the chance of mortality. Higher age values lead to death, whereas lower age values lead to survival. The impact of other features on the prediction can be analyzed in the same manner. The local interpretability of SHAP made the ML models more transparent by explaining why a case receives its prediction and the contributions of the features to the forecast. Below, we analyze some instances using SHAP to observe the prediction process of the ML model. Features that make a prediction of a positive outcome or death more likely are displayed in red, while those that make a prediction of a negative outcome or survival more likely are displayed in blue. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Year** & **Dataset** & **Sample** & **Algorithm** & **Number of Features** & **AUROC Score** \\ \hline
[52] & 2021 & MIMIC-III & 25,659 & XGBoost & 66 & 82\% \\ \hline
[53] & 2022 & MIMIC-III & 6699 & Ensemble & 20 & 82.55\% \\ \hline
[36] & 2021 & MIMIC-III & 58,976 & Dynamic Ensemble & 28 & 83.91\% \\ \hline
[41] & 2020 & MIMIC-III & 16,688 & GB & 86 & 84\% \\ \hline
**RF + PSO** & **2023** & **MIMIC-III** & **1177** & **RF + FPA** & **21** & **83.0\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of performance ( AUROC) of different ML models proposed in the literature. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Year** & **Dataset** & **Sample** & **Algorithm** & **Number of Features** & **Accuracy** \\ \hline
[54] & 2019 & MIMIC-III & 5037 & Logistic Model Trees & 79 & 85.12\% \\ \hline
[35] & 2021 & MIMIC-III & 1177 & XGBoost & 20 & 76\% \\ \hline
[34] & 2019 & MIMIC-III & 19,044 & RF & 15 & 72.8\% \\ \hline
**LR + FPA** & **2023** & **MIMIC-III** & **1177** & **RF + FPA** & **21** & **92.80\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the performance (accuracy) of different ML models proposed in the literature. In interpretation 1 shown in Figure 7, the features that were crucial to predicting an instance are depicted in red and blue, with red denoting features that increased the model score and blue denoting features that decreased the score. The closer the feature is to the line separating red from blue, the more of an impact it had on the score, and the size of the bars indicates the extent of the impact.