Browsing by Author "Victor Wandera Lumumba"

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A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
(American Journal of Theoretical and Applied Statistics, 2024-08-24) Dennis Muriithi; Victor Wandera Lumumba; Mark Okongo
For many years’ malaria has been a health public concern in Kenya as well as many parts of Africa and other parts of the world. 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 the least 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.
A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
(American Journal of Theoretical and Applied Statistics, 2024-08-20) Dennis Muriithi; Victor Wandera Lumumba; Mark Okongo
For many years’ malaria has been a health public concern in Kenya as well as many parts of Africa and other parts of the world. 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 the least 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.
Explainable Artificial Intelligence Models for Predicting Malaria Risk in Kenya
(European Journal of Artificial Intelligence and Machine Learning, 2025-02-12) Dennis Kariuki Muriithi; Victor Wandera Lumumba; Olushina Olawale Awe; Daniel Mwangi Muriithi
The article aims to develop interpretable Machine Learning models using R statistical programming language for malaria risk prediction in Kenya, emphasizing leveraging Explainable AI (XAI) techniques to support targeted interventions and improve early detection mechanisms. The methodology involved using synthetic data with 1000 observations, employing over-sampling to address class imbalance, utilizing two machine learning algorithms (Random Forest and Extreme Gradient Boosting), applying cross-validation techniques, Hyper-parameter tuning and implementing feature importance and SHAP (Shapley Additive Explanations) for model interpretability. The findings revealed that Random Forest outperformed Extreme Gradient Boosting with 98% accuracy. Critical prediction features included clinical symptoms such as nausea, muscle aches, and fever, plasmodium species identification, and environmental factors like rainfall and temperature. Both models demonstrated strong sensitivity in detecting malaria cases. This promotes trust in model predictions by clearly outlining the decision process for individual outcomes. The research concluded that integrating Explainable AI into malaria risk prediction represents a transformative approach to public health management. Through providing transparent, interpretable models, the research offers a robust, data-driven approach to predicting malaria risks, potentially empowering healthcare providers and policymakers to deploy resources more effectively and reduce the disease burden in endemic regions.