Integrating machine learning and spatial clustering for malaria case prediction in Brazil’s Legal Amazon

Abstract

Malaria remains a major global health challenge, particularly in Brazil’s Legal Amazon region, where environmental and socioeconomic conditions foster favorable conditions for disease transmission. Traditional control measures have shown limited effectiveness, emphasizing the need for better predictive approaches to support timely and targeted public health interventions. This study evaluates the performance of six computational models—Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Autoregressive Integrated Moving Average (ARIMA)—for forecasting weekly malaria cases across multiple states in the Legal Amazon. The results demonstrate that the RF model consistently outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values in most cases, such as in cluster 02 of the state of Acre, with RMSE of 0.00203 and MAE of 0.00133. The integration of K-means clustering further improved the model predictive accuracy by accounting for spatial heterogeneity and capturing localized transmission dynamics. This hybrid modeling approach, combining machine learning models with spatial clustering, offers a promising tool for enhancing malaria surveillance and guiding more effective public health strategies, especially for malaria control efforts in high-risk regions.