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the 9 studies using ML, the highest percentage targeted at the prognosis of obesity (n = 4, 44 %). In the studies incorporating more than one ML algorithms and reporting accuracy, it was shown that decision trees and artificial neural networks can accurately predict childhood obesity. Conclusions This review has found that CDS tools can be useful for the self-management or remote medical management of childhood obesity, whereas ML algorithms such as decision trees and artificial neural networks can be helpful for prediction purposes. F
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