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Table 2 Result analysis of OBCSA-OSAE model with different measures

From: A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model

No. of Runs Precision Recall Accuracy F-Score MCC Error Rate
Run-1 0.9808 0.9212 0.9123 0.9500 0.6143 0.0877
Run-2 0.9800 0.9346 0.9235 0.9568 0.6414 0.0765
Run-3 0.9780 0.9498 0.9352 0.9637 0.6697 0.0648
Run-4 0.9748 0.9558 0.9376 0.9652 0.6674 0.0624
Run-5 0.9842 0.9546 0.9450 0.9692 0.7230 0.0550
Run-6 0.9868 0.9438 0.9376 0.9648 0.7069 0.0624
Run-7 0.9850 0.9563 0.9473 0.9704 0.7335 0.0527
Run-8 0.9860 0.9568 0.9486 0.9712 0.7414 0.0514
Run-9 0.9896 0.9548 0.9500 0.9719 0.7565 0.0500
Run-10 0.9882 0.9531 0.9473 0.9704 0.7431 0.0527
Average 0.9833 0.9481 0.9384 0.9654 0.6997 0.0616