PREDICTION OF THE SPREAD OF MALARIA IN PLATEAU STATE: A MACHINE LEARNING APPROACH

Abstract

Malaria remains a major public health concern in Plateau State, Nigeria, with seasonal surges driven by climatic, environmental, and socio-economic factors. Despite various control interventions, locally adapted predictive models are scarce, limiting proactive disease control measures. This study aimed to develop and evaluate machine learning models capable of forecasting malaria incidence across the state, thereby supporting targeted prevention and control strategies. Using a ten-year dataset (2014– 2023) covering confirmed malaria cases, rainfall, temperature, and relative humidity for all 17 Local Government Areas (LGAs) of Plateau State, the data were preprocessed through cleaning, normalization, and integration of climatic and epidemiological variables. Three supervised machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)— were trained for both regression and classification tasks, and their performance was evaluated using Mean Squared Error (MSE), Coefficient of Determination (R2), accuracy, precision, recall, and F1-score. For classification, the Random Forest model achieved the highest accuracy (63.4%) with balanced precision and recall, followed by XGBoost, while SVM exhibited higher recall for class 0 but markedly lower performance for class 1. For regression, XGBoost outperformed all models, yielding the lowest MSE (554,539) and highest R2 (0.587), followed by Random Forest (R2 = 0.562), while SVM recorded a negative R2 (-0.037), indicating poor fit. The study concludes that tree-based ensemble models, particularly XGBoost, offer superior predictive capabilities for malaria incidence in Plateau State. It is recommended that such predictive models be integrated into the state’s malaria surveillance systems, retrained periodically with updated climatic and epidemiological data, and expanded to include socio-economic and intervention coverage variables for improved accuracy and operational relevance