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This study develops a machine learning model to predict discordance between rapid diagnostic tests (RDT) and microscopy in malaria surveillance in Bayelsa State, Nigeria, using a dataset of 2,100 observations from January 2019 to December 2024. The model, utilizing gradient boosting and SHAP analysis, identifies key predictors of discordance, revealing significant influences from rainfall, climate index, geographic location, and humidity. The findings aim to enhance malaria diagnosis accuracy and inform qua

Author

International Journal of Innovative Science and Research Technology

Language

EN