## 5 A LIME-Based Explainability Model  
Recent studies have introduced new explainability methods [22] that can explain the predictions of machine learning classifiers in an interpretable and accurate manner. As part of this study, we extended our machine learning model by using the Local Interpretable Model-agnostic Explanations (LIME) model [37]. The LIME model can explain inferences produced by machine learning models by performing a local approximation of the inference point. The LIME algorithm builds a linear regression in proximity to a specific inference point (that needs to be explained). Features with high positive weight in the linear regression approximation support the prediction decision, while features with negative weight oppose the decision. The original LIME model incurs stability issue, i.e., when the model is employed recurrently, under the same conditions, it may return different explanations. To overcome the stability issue, we use an enhanced type of the LIME model with statistical stability indices (available at [[https://github.com/giorgiovisani/LIME_stability](https://github.com/giorgiovisani/LIME_stability)]([https://github.com/giorgiovisani/LIME_stability](https://github.com/giorgiovisani/LIME_stability)) 28 October 2021) which has been introduced by [PERSON] [38].  
We demonstrate the usage of the LIME model on the MLP model A by choosing a rather complex case for analysis. Figure 4 exemplifies an explanation of a patient diagnosed with COVID-19. The patient's medical history and symptoms on arrival at the hospital are summarized in Figure 4a. The patient is over 70 years old with a medical history including pneumonia, diabetes, chronical renal failure, and cardiovascular disease. Our machine learning model (MLP model A) predicts the patient's severity of illness to be high. The LIME explanation is illustrated in Figure 4b. The blue bars illustrate the medical background and symptoms that have significant weight in supporting the prediction, while the green bars illustrate factors that comprise evidence against it. The explanation indicates that at the time of the prediction, the patient's main symptoms and medical history that most contribute to the prediction are pneumonia, age, diabetes, chronical renal failure, cardiovascular disease, asthma, other diseases, and exposure to other patients diagnosed with COVID-19.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Model** & **Prediction Accuracy** \\ \hline MLP Model A & 80.0\% \\ MLP Model B & 78.0\% \\ RF Model A & 80.0\% \\ RF Model B & 65.0\% \\ Binary regression & 76.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction Accuracy summary measured on the validation dataset.  
We compare the explanation provided by the LIME model with our binary regression model in Section 4. The independent variables in the binary regression equation are presented in Figure 4c with their corresponding weights (B) and statistical significance (Sig.). Figure 4c indicates that sex, pneumonia, age, diabetes, asthma, immunity system suppression, and obesity are significant contributors to a high severity of illness. One may notice that factors such as chronical renal failure, cardiovascular disease, and exposure to other COVID-19 patients have not been considered as major contributors to severe illness by the binary regression model. This is in contrast to our explainable machine learning model, which has identified these factors as contributing to a high severity level of illness in the described case analysis. This example demonstrates the limitation of binary regression in explaining complex morbidity. While the LIME model provides explainability per individual patients, the binary regression model provides a global statistical explainability.  
We summarize this discussion by indicating that such an explainability capability introduces new opportunities to help physicians identify the factors that most influence a patient's severity of illness and deterioration.