Geographically Weighted Machine Learning Model for Untangling Spatial Heterogeneity of Dengue Incidence in West Java

Abstract

Dengue hemorrhagic fever continues to pose a significant public health challenge, particularly in West Java Province, Indonesia, which consistently reports the highest incidence rates in the country. This study examined the factors influencing dengue fever incidence using Random Forest Regression (RFR) and Geographically Weighted Random Forest (GWRF) methodologies. Utilizing secondary data from 2022 to 2024 across 27 districts/cities, the data from 2022 to 2023 served as training data, while the 2024 data were used for testing. The findings revealed that the optimal RFR model, with ntree = 1000 and mtry = 1, achieved an RMSE of 1796.409, a MAPE of 0.482, and an  of 0.685. Conversely, the GWRF model, which employed an adaptive kernel and an optimal bandwidth of 45 nearest neighbors, exhibited superior performance, with an RMSE of 1,756.713, MAPE of 0.466, and  of 0.700. This enhancement in the model performance suggests that spatial weighting improves the model's capacity to capture spatial heterogeneity. In addition, variations in local feature importance indicate spatial non-stationarity across regions. These results imply that the GWRF is more effective in modeling dengue fever outbreaks and can inform the development of region-specific public health interventions.