The global healthcare systems have faced unprecedented challenges due to the COVID-19 pandemic, necessitating innovative neural computing solutions to inform critical decision-making. In this study, we introduce a neural-inspired machine learning framework to predict COVID-19 mortality risk, utilizing a dataset comprising over one million records. We developed and evaluated a suite of advanced models—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Gradient Boost Classifier, and a neural ensemble-based Voting Classifier—to analyze the influence of demographics, symptoms, and preexisting conditions on mortality predictions. Through meticulous feature engineering... More >