LightGBM and Voting Classifier: Top Performers in Supervised Classification for Vector-Borne Diseases in Hauts-Bassins, Burkina Faso

International audience Background Vector-borne diseases (VBDs) represent a major public health challenge [1] , particularly affecting low-resource regions where access to conventional diagnostic tools remains limited [2] . Malaria, along with other VBDs such as dengue and yellow fever, leads to high morbidity and mortality rates, placing significant pressure on healthcare systems. Traditional diagnostic methods often require specialized equipment, laboratory facilities, or expert interpretation, which are scarce in many endemic areas. These limitations highlight the urgent need for alternative diagnostic approaches that are accurate, efficient, and applicable in resource-constrained environments.