## 5 Discussion  
A great deal of improvements has been made in diagnosing, detecting, and preventing hepatitis B. From clinical, statistical, and machine learning, these improvements focus on providing a lasting solution to the menace of HBV. Differentiating hepatitis B from hepatitis caused by other viral agents may not be clinically possible except through laboratory confirmation of the diagnosis.  
Different biochemical and virological factors can be used to identify liver damage and viral activity in a patient infected with hepatitis B [12]. This diagnosis can differentiate between acute and chronic hepatitis B infections. However, research has shown that statistical methods can predict acute and chronic HBV using clinical data. For instance, in [PERSON] et al. [73], clinical data of 1373 patients with acute deterioration of HBV chronic liver disease were used to identify the clinical characteristics of HBV. The statistical model produces a prognostic score for the onset of Acute-on-chronic liver failure. [PERSON] et al. [73] suggest that statistical methods can accurately predict HBV-related chronic liver disease and can be used as a guide for clinical management [73]. Similarly, with the hepatitis B dataset, we used machine learning algorithms, such as decision trees, logistic regression, SVM, random forest, XGBoost, and AdaBoost, to identify patterns and features that show unique relationships between covariates. Our result corroborates with the work in [74] that indicates that machine learning can identify and predict the risk of HBV.  
Unlike other studies that use machine learning to diagnose HBV, such as [74; 75; 76], the decision tree classifier identified bilirubin as having the highest variability in SHAP values (see Figure 1). Since bilirubin is produced from the breakdown of red blood cells  
Figure 3: Summary plot of the predictions.  
in the human body. A higher bilirubin level suggests liver disease or damage, indicating the presence of HBV or other problems. The interpretation of the machine learning model increases the model's efficacy transparency and promotes increased confidence in the approach, especially for the medical practitioner or health policy maker. In addition to the bilirubin identified by the decision tree model, other features contributing to HBV death include high ascites levels, age, alkaline, and malaise levels, as summarised in Figure 2. Such information can be helpful to health practitioners or health policymakers in making decisions at a glance from the visualized model interpretation summary.  
A similar study in [77] used the CRISP-ML method to determine the interpretability level required by stakeholders for a successful real-world solution. CRISP-ML approach differs from our study because it was used to develop an interpretation requirement for data diversity problems and to manage the unstructured nature of data in the healthcare domain. Similarly, we compared our work with the state-of-the-art machine learning models used for hepatitis B predictability, as summarized in Table 3. Of the studies reviewed, only the work of [PERSON] et al. [78] considered model explainability to the Gradient Boosting Machine (GBM) algorithm. The remaining studies never considered this option.  
The more the feature of a dataset increases, the better the performance of the model. One major limitation of this study is the lack of available large data size for hepatitis B. However, our study focused more on the interpretability of the model. In the future, we recommend an increased clinical dataset size for hepatitis B to assist in improving the performance of the machine learning model, especially during the model training time.