Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa.

Our results were best with those made in Uganda, which indicated that the random forest model was highly significant for predicting childhood ARI symptoms with an accuracy of 88.70% ( 33 ).

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