A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

dc.contributor.authorDennis Muriithi* , Victor Wandera Lumumba , Mark Okongo
dc.date.accessioned2025-03-05T11:15:00Z
dc.date.available2025-03-05T11:15:00Z
dc.date.issued2024-08-20
dc.descriptionResearch article
dc.description.abstractThe 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.
dc.identifier.citationMuriithi, 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.
dc.identifier.urihttps://doi.org/10.11648/j.ajtas.20241304.11
dc.identifier.urihttps://repository.chuka.ac.ke/handle/123456789/16648
dc.language.isoen
dc.publisherAmerican Journal of Theoretical and Applied Statistics
dc.subjectMachine Learning
dc.subjectAccuracy
dc.subjectSensitivity
dc.subjectSpecificity
dc.subjectFeature
dc.subjectBalance Accuracy
dc.subjectMalaria
dc.titleA Machine Learning-Based Prediction of Malaria Occurrence in Kenya
dc.typeArticle

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