Machine learning-enabled classification of global human development using INFORM risk indicators

This study aims to identify the most effective machine learning model for classifying countries' Human Development Index (HDI) levels using indicators from the INFORM Risk Index. The motivation for this work lies in the growing need for data-driven methods to analyze and predict human development outcomes, particularly in the context of complex and high-dimensional socio-economic and disaster-related risk data. Traditional models often fail to capture the non-linear relationships that influence human development. To address this gap, six supervised machine learning algorithms—k-Nearest Neighbors (KNN), Linear and Nonlinear Support Vector Machines (SVM), Classification and Regression Trees (CART), Bagging, and Random Forest (RF)—were systematically evaluated. Performance was measured using weighted F1-scores on both training and testing datasets. The results reveal that while KNN, Linear SVM, and CART have limited predictive power, the Nonlinear SVM suffers from overfitting. In contrast, ensemble-based models—Bagging and RF—demonstrate superior and balanced performance, with F1-scores around 0.80 on both datasets. These methods also allow for interpretability through feature importance analysis. Socio-economic, institutional, and infrastructure-related indicators were identified as the most influential variables in predicting HDI levels. The findings highlight the strength of ensemble learning in modeling complex development-related risks and provide a robust framework for integrating machine learning into global human development analysis. This study offers valuable insights for policymakers and researchers aiming to improve forecasting, resilience planning, and development strategies.