The effect of data balancing approaches on the prediction of metabolic syndrome using non-invasive parameters based on random forest.

The random forest learning method, along with data balancing techniques, especially SplitBal, could create MetS prediction models with promising results that can be applied as a useful prognostic tool in health screening programs.