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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 …