TY - GEN
T1 - Predicting Diarrhoea Among Children Under Five Years Using Machine Learning Techniques
AU - Mbunge, Elliot
AU - Chemhaka, Garikayi
AU - Batani, John
AU - Gurajena, Caroline
AU - Dzinamarira, Tafadzwa
AU - Musuka, Godfrey
AU - Chingombe, Innocent
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Globally, diarrhoea remains a significant cause of death among children under five years. Several preventive interventions such as hygiene practice, safe drinking water, rotavirus vaccination and health promotion were implemented to reduce the catastrophic impact of diarrhoea. However, effective tackling of the diarrhoeal disease requires robust preventive interventions and computational techniques to predict diarrhoea among children under five years using risk factors. Therefore, this study applied a decision tree classifier, logistic regression and support vector machines to predict diarrhoea among children under five years using the recent Zimbabwe Demographic Health Survey dataset. The study revealed that logistic regression out-performed other diarrhoea predictive models with the prediction accuracy of 85%, precision of 86%, recall of 100% and the F1-score of 94%. Support vector machines also performed better in predicting diarrhoea with predicting accuracy of 84%, precision of 85%, recall of 100% and F1-score of 92%. The study also revealed that understanding risk factors such as climatic or meteorological, socioeconomic and demographic factors plays a tremendous role in tackling diarrhoea among under-fives. The application of machine learning techniques can assist policymakers in designing effective and adaptive diarrhoea preventive interventions, control programmes and strategies for tackling diarrhoea.
AB - Globally, diarrhoea remains a significant cause of death among children under five years. Several preventive interventions such as hygiene practice, safe drinking water, rotavirus vaccination and health promotion were implemented to reduce the catastrophic impact of diarrhoea. However, effective tackling of the diarrhoeal disease requires robust preventive interventions and computational techniques to predict diarrhoea among children under five years using risk factors. Therefore, this study applied a decision tree classifier, logistic regression and support vector machines to predict diarrhoea among children under five years using the recent Zimbabwe Demographic Health Survey dataset. The study revealed that logistic regression out-performed other diarrhoea predictive models with the prediction accuracy of 85%, precision of 86%, recall of 100% and the F1-score of 94%. Support vector machines also performed better in predicting diarrhoea with predicting accuracy of 84%, precision of 85%, recall of 100% and F1-score of 92%. The study also revealed that understanding risk factors such as climatic or meteorological, socioeconomic and demographic factors plays a tremendous role in tackling diarrhoea among under-fives. The application of machine learning techniques can assist policymakers in designing effective and adaptive diarrhoea preventive interventions, control programmes and strategies for tackling diarrhoea.
KW - Children under-five
KW - Diarrhoea
KW - Machine learning
KW - Prediction
KW - Zimbabwe
UR - http://www.scopus.com/inward/record.url?scp=85135022572&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-09076-9_9
DO - 10.1007/978-3-031-09076-9_9
M3 - Conference contribution
AN - SCOPUS:85135022572
SN - 9783031090752
T3 - Lecture Notes in Networks and Systems
SP - 94
EP - 109
BT - Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2
A2 - Silhavy, Radek
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Computer Science On-line Conference, CSOC 2022
Y2 - 26 April 2022 through 26 April 2022
ER -
