A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
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Date
2024-08-20
Journal Title
Journal ISSN
Volume Title
Publisher
American Journal of Theoretical and Applied Statistics
Abstract
The purpose of this study is to develop and evaluate a supervised machine learning model
to predict malaria occurrence (final malaria test results) in Kenya. The study investigated
twelve predictor variables on the outcome variable (malaria test results), where five
machine learning models namely; k-nearest neighbors, support vector machines, random
forest, tree bagging, and boosting, were estimated. During the model evaluation, random
forest emerged as the best overall model in the classification and prediction of final malaria
test results. The model attained a higher classification accuracy of 97.33%, sensitivity of
71.1%, specificity of 98.4%, balanced accuracy of 84.7% and an area under the curve of
98.3%. From the final model, the presence of plasmodium falciparum emerged most
important feature, followed by region, endemic zone and anemic level. The feature with theleast importance in predicting final malaria test results was having mosquito nets. In
conclusion, employing Machine learning algorithms enhances early detection, optimizing
resource allocation for interventions, and ultimately reducing the incidence and impact of
malaria in the Kenya. The study recommends allocation of resources and funds to areas
with the presence of plasmodium falciparum, region susceptible to malaria, endemic zones
and anemic prone areas.
Description
Research article
Keywords
Machine Learning, Accuracy, Sensitivity, Specificity, Feature, Balance Accuracy, Malaria
Citation
Muriithi, D., Lumumba, V., & Okongo, M. (2024). A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. American Journal of Theoretical and Applied Statistics, 13(4), 65-72.