. Author manuscript; available in PMC: 2022 May 1.

Published in final edited form as: Surgery. 2020 Nov 5;169(5):1245–1249. doi: 10.1016/j.surg.2020.09.020

Table 2.

Performance accuracy when distinguishing super-experts vs. ordinary-experts (Comparison 2).

Machine learning model	Performance accuracy comparison between varying datasets				p Value
Adaboost	ColumnSet	0.801 ± 0.014	RowSet	0.772 ± 0.009	p < 0.001
Adaboost	RowSet	0.772 ± 0.009	GlobalSet	0.774 ± 0.009	p = 0.14
Gradient Boosting	ColumnSet	0.770 ± 0.006	RowSet	0.784 ± 0.006	p < 0.001
Gradient Boosting	RowSet	0.784 ± 0.006	GlobalSet	0.759 ± 0.002	p < 0.001
Random Forest	ColumnSet	0.761 ± 0.007	RowSet	0.761 ± 0.004	p = 0.959
Random Forest	RowSet	0.761 ± 0.004	GlobalSet	0.769 ± 0.009	p = < 0.001