Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of model interpretability is limited in the existing literature. Model interpretability makes it easier for humans to understand and trust the machine-learning model. Therefore, in this study, we used SHapley Additive exPlanations (SHAP), a game-based theoretical approach to explain and visualize the predictions of machine learning models applied for hepatitis B diagnosis. The algorithms used in building the models include decision tree, logistic regression, support vector machines, random forest, adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost), and they achieved balanced accuracies of 75%, 82%, 75%, 86%, 92%, and 90%, respectively. Meanwhile, the SHAP values showed that bilirubin is the most significant feature contributing to a higher mortality rate. Consequently, older patients are more likely to die with elevated bilirubin levels. The outcome of this study can aid health practitioners and health policymakers in explaining the result of machine learning models for health-related problems.

Diabetic ketoacidosis (DKA) is a serious complication that affects millions of individuals globally and presents significant health complications. Hyperchloremia, an electrolyte imbalance characterized by high levels of chloride in the blood, may result in gastrointestinal problems, kidney damage, and even death, especially in DKA patients. Early detection and treatment of hyperchloremia are of utmost importance in the management of DKA. This study explores the potential of the bootstrap aggregating ensemble with random subspaces machine learning approach to predict the occurrence of hyperchloremia, providing a basis for early intervention and improved patient outcomes. We tested our approach with the retrospective MIMIC-III database containing 1177 DKA patients and compared it with previous studies with an area under the curve (AUC) of 100%. Our approach showed significant performance outperforming other methods. The combination of this approach may enhance the early detection and timely intervention of hyperchloremia cases, ultimately leading to improved patient outcomes and a more effective management of DKA-associated complications. Our work aims to contribute to the development of decision support tools for healthcare professionals, assisting them in making informed decisions for DKA patients, with a focus on preventing and managing hyperchloremia.