The study of under- and over-sampling methods' utility in analysis of highly imbalanced data on osteoporosis

The outcomes of the comparative studies concerning imbalancing problem with regard to our dataset showed that the highest efficiency was achieved while using the synthetic minority over-sampling technique and RandomForest classifier. As far as the optimal balancing level is concerned, we empirically determined that 300% oversampling with the synthetic minority over-sampling method combined with edited nearest neighbours undersampling allowed to gain the required precision of classification.