A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

Dennis Muriithi; Victor Wandera Lumumba; Mark Okongo

doi:doi:10.11648/j.ajtas.20241304.11

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A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

Dennis Muriithi^*

, Victor Wandera Lumumba

, Mark Okongo

Published in American Journal of Theoretical and Applied Statistics (Volume 13, Issue 4)

Received: 20 July 2024 Accepted: 9 August 2024 Published: 20 August 2024

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 13, Issue 4)
DOI	10.11648/j.ajtas.20241304.11
Page(s)	65-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Accuracy, Sensitivity, Specificity, Feature, Balance Accuracy, Malaria

References

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APA Style

Muriithi, D., Lumumba, V. W., Okongo, M. (2024). A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. American Journal of Theoretical and Applied Statistics, 13(4), 65-72. https://doi.org/10.11648/j.ajtas.20241304.11

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ACS Style

Muriithi, D.; Lumumba, V. W.; Okongo, M. A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. Am. J. Theor. Appl. Stat. 2024, 13(4), 65-72. doi: 10.11648/j.ajtas.20241304.11

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AMA Style

Muriithi D, Lumumba VW, Okongo M. A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. Am J Theor Appl Stat. 2024;13(4):65-72. doi: 10.11648/j.ajtas.20241304.11

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@article{10.11648/j.ajtas.20241304.11,
  author = {Dennis Muriithi and Victor Wandera Lumumba and Mark Okongo},
  title = {A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {13},
  number = {4},
  pages = {65-72},
  doi = {10.11648/j.ajtas.20241304.11},
  url = {https://doi.org/10.11648/j.ajtas.20241304.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241304.11},
  abstract = {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.
},
 year = {2024}
}

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TY  - JOUR
T1  - A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

AU  - Dennis Muriithi
AU  - Victor Wandera Lumumba
AU  - Mark Okongo
Y1  - 2024/08/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajtas.20241304.11
DO  - 10.11648/j.ajtas.20241304.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 65
EP  - 72
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20241304.11
AB  - 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.

VL  - 13
IS  - 4
ER  -

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Author Information

Dennis Muriithi

Center for Data Analytics and Modelling, Faculty of Science and Technology, Chuka University, Chuka, Kenya

Contact Email

http://orcid.org/0000-0002-3210-0925
Victor Wandera Lumumba

Center for Data Analytics and Modelling, Faculty of Science and Technology, Chuka University, Chuka, Kenya

Contact Email

http://orcid.org/0009-0000-2840-8364
Mark Okongo

Center for Data Analytics and Modelling, Faculty of Science and Technology, Chuka University, Chuka, Kenya

Contact Email

http://orcid.org/0000-0002-4198-9594

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APA Style

Muriithi, D., Lumumba, V. W., Okongo, M. (2024). A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. American Journal of Theoretical and Applied Statistics, 13(4), 65-72. https://doi.org/10.11648/j.ajtas.20241304.11

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ACS Style

Muriithi, D.; Lumumba, V. W.; Okongo, M. A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. Am. J. Theor. Appl. Stat. 2024, 13(4), 65-72. doi: 10.11648/j.ajtas.20241304.11

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AMA Style

Muriithi D, Lumumba VW, Okongo M. A Machine Learning-Based Prediction of Malaria Occurrence in Kenya. Am J Theor Appl Stat. 2024;13(4):65-72. doi: 10.11648/j.ajtas.20241304.11

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@article{10.11648/j.ajtas.20241304.11,
  author = {Dennis Muriithi and Victor Wandera Lumumba and Mark Okongo},
  title = {A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {13},
  number = {4},
  pages = {65-72},
  doi = {10.11648/j.ajtas.20241304.11},
  url = {https://doi.org/10.11648/j.ajtas.20241304.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241304.11},
  abstract = {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.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

AU  - Dennis Muriithi
AU  - Victor Wandera Lumumba
AU  - Mark Okongo
Y1  - 2024/08/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajtas.20241304.11
DO  - 10.11648/j.ajtas.20241304.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 65
EP  - 72
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20241304.11
AB  - 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.

VL  - 13
IS  - 4
ER  -

Copy | Download