TY - GEN
T1 - Application of machine learning models to predict malaria using malaria cases and environmental risk factors
AU - Mbunge, Elliot
AU - Millham, Richard C.
AU - Sibiya, Maureen Nokuthula
AU - Takavarasha, Sam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Malaria remains a significant cause of deaths and illness especially in sub-Saharan Africa (SSA). The efforts to eliminate malaria include the use of intermittent preventive prophylaxis (ITPp), indoor residual spraying (IRS), long-lasting insecticide-Treated nets (LLINs), malaria prevention strategies and behavioural change education. Among these initiatives, predicting malaria cases at the ward level tremendously assist in malaria elimination, yet its application is still low. Therefore, this paper applied logistic regression, decision trees classifier, support vector machine, and random forest classifier to predict malaria in Buhera district. The study shows that logistic regression performs better, with 83% accuracy, 82% precision and 90% F1-score than other machine learning classifiers when predicting malaria outbreaks using environmental risk factors. These models can aid decision-makers to effectively allocate resources, development of malaria early warning systems, optimize the distribution of indoor residual spraying teams and spraying equipment, giving more priority to high sporadic areas.
AB - Malaria remains a significant cause of deaths and illness especially in sub-Saharan Africa (SSA). The efforts to eliminate malaria include the use of intermittent preventive prophylaxis (ITPp), indoor residual spraying (IRS), long-lasting insecticide-Treated nets (LLINs), malaria prevention strategies and behavioural change education. Among these initiatives, predicting malaria cases at the ward level tremendously assist in malaria elimination, yet its application is still low. Therefore, this paper applied logistic regression, decision trees classifier, support vector machine, and random forest classifier to predict malaria in Buhera district. The study shows that logistic regression performs better, with 83% accuracy, 82% precision and 90% F1-score than other machine learning classifiers when predicting malaria outbreaks using environmental risk factors. These models can aid decision-makers to effectively allocate resources, development of malaria early warning systems, optimize the distribution of indoor residual spraying teams and spraying equipment, giving more priority to high sporadic areas.
KW - Environmental risk factors
KW - Machine learning
KW - Malaria
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85128663845&partnerID=8YFLogxK
U2 - 10.1109/ICTAS53252.2022.9744657
DO - 10.1109/ICTAS53252.2022.9744657
M3 - Conference contribution
AN - SCOPUS:85128663845
T3 - 2022 Conference on Information Communications Technology and Society, ICTAS 2022 - Proceedings
BT - 2022 Conference on Information Communications Technology and Society, ICTAS 2022 - Proceedings
A2 - Millham, R.C.
A2 - Heukelman, D.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th Annual International Conference on Information Communications Technology and Society, ICTAS 2022
Y2 - 9 March 2022 through 10 March 2022
ER -
