Machine Learning-Driven Risk Stratification in Rural Healthcare During the Pandemic: An Ensemble Voting Classifier Approach

This research presents an Ensemble Voting Classifier that is aimed at enhancing the accessibility of healthcare in rural areas during the pandemic. The model applies a range of machine learning algorithms to make timely interventions and classify patients according to their risk levels. It was evaluated with an impressive AUC-ROC of 98.64% and an accuracy of 97.58% on a comprehensive dataset. These indicators are shown to be able to detect patients at a greater risk of poor health outcomes when the resources are scarcer, an important component in reducing health disparities. Ensemble learning methodologies may be helpful in the decision-making of rural health care since the approach developed in this paper exceeds the state of the art. The study results indicate the promise of applying machine learning to enhance healthcare outcomes and optimize triaging patients in case of public health emergencies, hence more equal access to care for vulnerable populations.