Predictive Modeling of Malaria Risk Using the Nigerian Demographic and Health Survey Data

Abstract

Malaria continues to pose a significant public health challenge in Nigeria, yet there has not been much research utilizing machine-learning techniques to forecast malaria risk. This study developed a machine-learning model that predicts malaria risk by leveraging demographic, environmental, and GPS data from the Nigerian Demographic and Health Survey (DHS) covering the years 2000 to 2020. The dataset was pre-processed and split into a training set (with 406 respondents) and a test set (with 102 respondents). Random Forest (RF), Gradient Boosting (GB) and Linear Regression (LR) algorithms were employed to assess their predictive performance. The RF stood out with the best accuracy, achieving the lowest mean squared error (MSE = 0.0053) and the highest coefficient of determination (R2 = 0.6364). Thus, RF was recognized as the most effective model for predicting malaria risk. The regression equation with positive coefficients (like population density = 0.0141, travel time = 0.0019, minimum temperature = 0.0082, temperature in January = 0.0265, and dry land surface temp = 0.0368) indicate that higher feature values are associated with increased malaria prevalence, while negative coefficients (such as rainfall = −0.0122, nightlights composite = −0.03, potential evapotranspiration = −0.09 and insecticide treated nets = −0.02) suggest that as the feature increases, the prevalence decreases. This study underscores the potential of the RF approach in improving early predictions of malaria risk and can guide targeted interventions to control malaria in areas at high risk.