Using Country-Level Variables to Discover Country Clusters Beyond Traditional Health Policy and Performance Metrics: An Unsupervised Machine Learning Approach for HIV Healthcare Delivery and Financing

This study applies unsupervised machine learning techniques to identify clusters of countries with similar HIV healthcare delivery and financing characteristics. Moving beyond traditional health policy and performance indicators, the authors use multidimensional country-level variables to uncover patterns in health system organization, financing structures, and service delivery approaches. The findings demonstrate how machine learning methods can support comparative health systems analysis and help policymakers identify peer-country models and contextually relevant strategies for strengthening HIV care and financing systems.