Table 2. Summary of model metrics for four machine-learning techniques.

XGBoost
Metrics	Minimum	5th Percentile	25th Percentile	Median	75th Percentile	95th Percentile	Maximum	Mean	Standard Deviation	Range
Accuracy	0.688	0.744	0.771	0.79	0.808	0.832	0.894	0.789	0.027	0.206
F1	0.69	0.745	0.772	0.788	0.81	0.832	0.897	0.79	0.027	0.207
Sensitivity	0.678	0.759	0.788	0.808	0.825	0.85	0.906	0.806	0.028	0.228
Specificity	0.595	0.709	0.753	0.785	0.814	0.855	0.944	0.784	0.042	0.349
PPV	0.68	0.757	0.786	0.82	0.845	0.88	0.954	0.82	0.037	0.274
NPV	0.57	0.678	0.725	0.756	0.787	0.83	0.928	0.756	0.046	0.358
AUROC	0.771	0.828	0.853	0.87	0.885	0.906	0.947	0.869	0.023	0.176
Random Forest
Metrics	Minimum	5th Percentile	25th Percentile	Median	75th Percentile	95th Percentile	Maximum	Mean	Standard Deviation	Range
Accuracy	0.670	0.728	0.768	0.782	0.800	0.815	0.889	0.784	0.026	0.219
F1	0.683	0.736	0.772	0.781	0.806	0.815	0.880	0.786	0.026	0.196
Sensitivity	0.663	0.747	0.784	0.797	0.807	0.846	0.893	0.797	0.029	0.229
Specificity	0.584	0.708	0.743	0.784	0.807	0.845	0.925	0.774	0.042	0.340
PPV	0.673	0.741	0.778	0.808	0.842	0.862	0.947	0.806	0.041	0.274
NPV	0.551	0.658	0.716	0.740	0.769	0.829	0.911	0.754	0.042	0.360
AUROC	0.755	0.821	0.847	0.863	0.883	0.897	0.931	0.855	0.024	0.176
Artificial Neural Network
Metrics	Minimum	5th Percentile	25th Percentile	Median	75th Percentile	95th Percentile	Maximum	Mean	Standard Deviation	Range
Accuracy	0.687	0.740	0.760	0.784	0.804	0.828	0.880	0.776	0.023	0.193
F1	0.673	0.735	0.753	0.782	0.791	0.822	0.886	0.774	0.025	0.212
Sensitivity	0.672	0.747	0.776	0.797	0.806	0.832	0.888	0.796	0.024	0.217
Specificity	0.594	0.704	0.751	0.769	0.799	0.837	0.926	0.764	0.039	0.332
PPV	0.660	0.749	0.778	0.811	0.836	0.862	0.939	0.808	0.033	0.278
NPV	0.551	0.662	0.715	0.748	0.771	0.814	0.913	0.744	0.050	0.362
AUROC	0.752	0.819	0.838	0.862	0.882	0.889	0.946	0.851	0.025	0.194
Adaptive Boosting
Metrics	Minimum	5th Percentile	25th Percentile	Median	75th Percentile	95th Percentile	Maximum	Mean	Standard Deviation	Range
Accuracy	0.687	0.732	0.759	0.79	0.793	0.82	0.886	0.776	0.028	0.199
F1	0.67	0.743	0.758	0.769	0.806	0.826	0.892	0.775	0.025	0.221
Sensitivity	0.674	0.752	0.781	0.808	0.812	0.835	0.89	0.796	0.023	0.216
Specificity	0.589	0.692	0.744	0.778	0.803	0.853	0.944	0.776	0.041	0.355
PPV	0.672	0.743	0.774	0.8	0.845	0.862	0.948	0.816	0.04	0.276
NPV	0.567	0.661	0.714	0.749	0.786	0.826	0.925	0.749	0.042	0.358
AUROC	0.756	0.814	0.839	0.865	0.865	0.897	0.934	0.866	0.026	0.178

Summary of model metrics within the test set for each of the four machine-learning techniques (XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting).