Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis

Łukasz Lewandowski; Michał Czapla; Izabella Uchmanowicz; Grzegorz Kubielas; Stanisław Zieliński; Małgorzata Krzystek-Korpacka; Catherine Ross; Raúl Juárez-Vela; Marzena Zielińska

doi:10.12659/MSM.944408

10 August 2024 : Database Analysis

Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis

Łukasz Lewandowski

^1ABCDEF, Michał Czapla

^234ABDEF*, Izabella Uchmanowicz

^5DEG, Grzegorz Kubielas

^6E, Stanisław Zieliński

^7E, Małgorzata Krzystek-Korpacka

^1E, Catherine Ross

^8E, Raúl Juárez-Vela

^3E, Marzena Zielińska

^7BDE

DOI: 10.12659/MSM.944408

Med Sci Monit 2024; 30:e944408

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Supplementary Table 4 Selected metrics of the best logistic regression model (Supplementary Table 3: model 4).

Cut-off point	Recall	Specificity	Accuracy	Precision	NPV	FPR	FNR	Youden’s J statistic
0.998	0.033	1.000	0.477	1.000	0.468	0.000	0.967	0.033
0.995	0.050	1.000	0.486	1.000	0.472	0.000	0.950	0.050
0.993	0.067	1.000	0.495	1.000	0.477	0.000	0.933	0.067
0.984	0.083	0.980	0.495	0.833	0.476	0.020	0.917	0.064
0.976	0.100	0.980	0.505	0.857	0.481	0.020	0.900	0.080
0.975	0.117	0.980	0.514	0.875	0.485	0.020	0.883	0.097
0.972	0.133	0.980	0.523	0.889	0.490	0.020	0.867	0.114
0.953	0.133	0.961	0.514	0.800	0.485	0.039	0.867	0.094
0.937	0.150	0.961	0.523	0.818	0.490	0.039	0.850	0.111
0.922	0.183	0.961	0.541	0.846	0.500	0.039	0.817	0.144
0.913	0.200	0.961	0.550	0.857	0.505	0.039	0.800	0.161
0.903	0.217	0.961	0.559	0.867	0.510	0.039	0.783	0.177
0.901	0.233	0.961	0.568	0.875	0.516	0.039	0.767	0.194
0.871	0.250	0.961	0.577	0.882	0.521	0.039	0.750	0.211
0.864	0.267	0.961	0.586	0.889	0.527	0.039	0.733	0.227
0.861	0.283	0.961	0.595	0.895	0.533	0.039	0.717	0.244
0.852	0.300	0.961	0.604	0.900	0.538	0.039	0.700	0.261
0.837	0.317	0.961	0.613	0.905	0.544	0.039	0.683	0.277
0.833	0.333	0.961	0.622	0.909	0.551	0.039	0.667	0.294
0.827	0.350	0.961	0.631	0.913	0.557	0.039	0.650	0.311
0.819	0.367	0.961	0.640	0.917	0.563	0.039	0.633	0.327
0.817	0.400	0.961	0.658	0.923	0.576	0.039	0.600	0.361
0.801	0.417	0.961	0.667	0.926	0.583	0.039	0.583	0.377
0.792	0.417	0.941	0.658	0.893	0.578	0.059	0.583	0.358
0.79	0.417	0.922	0.649	0.862	0.573	0.078	0.583	0.338
0.778	0.433	0.922	0.658	0.867	0.580	0.078	0.567	0.355
0.768	0.450	0.922	0.667	0.871	0.588	0.078	0.550	0.372
0.759	0.467	0.922	0.676	0.875	0.595	0.078	0.533	0.388
0.742	0.483	0.922	0.685	0.879	0.603	0.078	0.517	0.405
0.734	0.500	0.902	0.685	0.857	0.605	0.098	0.500	0.402
0.718	0.517	0.902	0.694	0.861	0.613	0.098	0.483	0.419
0.714	0.533	0.902	0.703	0.865	0.622	0.098	0.467	0.435
0.710	0.550	0.902	0.712	0.868	0.630	0.098	0.450	0.452
0.707	0.567	0.902	0.721	0.872	0.639	0.098	0.433	0.469
0.702	0.583	0.902	0.730	0.875	0.648	0.098	0.417	0.485
0.669	0.583	0.882	0.721	0.854	0.643	0.118	0.417	0.466
0.662	0.583	0.863	0.712	0.833	0.638	0.137	0.417	0.446
0.65	0.583	0.843	0.703	0.814	0.632	0.157	0.417	0.426
0.645	0.600	0.843	0.712	0.818	0.642	0.157	0.400	0.443
0.636	0.617	0.843	0.721	0.822	0.652	0.157	0.383	0.460
0.634	0.633	0.843	0.730	0.826	0.662	0.157	0.367	0.476
0.632	0.650	0.843	0.739	0.830	0.672	0.157	0.350	0.493
0.629	0.650	0.824	0.730	0.813	0.667	0.176	0.350	0.474
0.624	0.650	0.804	0.721	0.796	0.661	0.196	0.350	0.454
0.604	0.650	0.784	0.712	0.780	0.656	0.216	0.350	0.434
0.593	0.650	0.765	0.703	0.765	0.650	0.235	0.350	0.415
0.585	0.650	0.745	0.694	0.750	0.644	0.255	0.350	0.395
0.564	0.667	0.745	0.703	0.755	0.655	0.255	0.333	0.412
0.551	0.683	0.745	0.712	0.759	0.667	0.255	0.317	0.428
0.538	0.700	0.745	0.721	0.764	0.679	0.255	0.300	0.445
0.531	0.700	0.725	0.712	0.750	0.673	0.275	0.300	0.425
0.521	0.700	0.706	0.703	0.737	0.667	0.294	0.300	0.406
0.513	0.717	0.706	0.712	0.741	0.679	0.294	0.283	0.423
0.512	0.717	0.667	0.694	0.717	0.667	0.333	0.283	0.383
0.508	0.733	0.667	0.703	0.721	0.680	0.333	0.267	0.400
0.501	0.733	0.647	0.694	0.710	0.673	0.353	0.267	0.380
0.498	0.750	0.647	0.703	0.714	0.688	0.353	0.250	0.397
0.487	0.767	0.647	0.712	0.719	0.702	0.353	0.233	0.414
0.480	0.767	0.627	0.703	0.708	0.696	0.373	0.233	0.394
0.477	0.767	0.608	0.694	0.697	0.689	0.392	0.233	0.375
0.466	0.767	0.588	0.685	0.687	0.682	0.412	0.233	0.355
0.45	0.783	0.588	0.694	0.691	0.698	0.412	0.217	0.372
0.426	0.783	0.569	0.685	0.681	0.690	0.431	0.217	0.352
0.418	0.800	0.569	0.694	0.686	0.707	0.431	0.200	0.369
0.417	0.800	0.549	0.685	0.676	0.700	0.451	0.200	0.349
0.405	0.817	0.549	0.694	0.681	0.718	0.451	0.183	0.366
0.402	0.833	0.549	0.703	0.685	0.737	0.451	0.167	0.382
0.394	0.833	0.529	0.694	0.676	0.730	0.471	0.167	0.363
0.363	0.833	0.510	0.685	0.667	0.722	0.490	0.167	0.343
0.361	0.833	0.490	0.676	0.658	0.714	0.510	0.167	0.324
0.347	0.833	0.471	0.667	0.649	0.706	0.529	0.167	0.304
0.342	0.850	0.471	0.676	0.654	0.727	0.529	0.150	0.321
0.34	0.850	0.451	0.667	0.646	0.719	0.549	0.150	0.301
0.332	0.867	0.451	0.676	0.650	0.742	0.549	0.133	0.318
0.320	0.867	0.431	0.667	0.642	0.733	0.569	0.133	0.298
0.311	0.883	0.431	0.676	0.646	0.759	0.569	0.117	0.315
0.309	0.883	0.412	0.667	0.639	0.750	0.588	0.117	0.295
0.296	0.883	0.392	0.658	0.631	0.741	0.608	0.117	0.275
0.286	0.883	0.373	0.649	0.624	0.731	0.627	0.117	0.256
0.281	0.883	0.353	0.640	0.616	0.720	0.647	0.117	0.236
0.279	0.883	0.333	0.631	0.609	0.708	0.667	0.117	0.217
0.276	0.883	0.314	0.622	0.602	0.696	0.686	0.117	0.197
0.272	0.883	0.294	0.613	0.596	0.682	0.706	0.117	0.177
0.262	0.883	0.275	0.604	0.589	0.667	0.725	0.117	0.158
0.244	0.883	0.255	0.595	0.582	0.650	0.745	0.117	0.138
0.237	0.883	0.235	0.586	0.576	0.632	0.765	0.117	0.119
0.229	0.883	0.216	0.577	0.570	0.611	0.784	0.117	0.099
0.228	0.883	0.196	0.568	0.564	0.588	0.804	0.117	0.079
0.22	0.900	0.196	0.577	0.568	0.625	0.804	0.100	0.096
0.216	0.917	0.196	0.586	0.573	0.667	0.804	0.083	0.113
0.213	0.917	0.176	0.577	0.567	0.643	0.824	0.083	0.093
0.206	0.917	0.157	0.568	0.561	0.615	0.843	0.083	0.074
0.199	0.917	0.137	0.559	0.556	0.583	0.863	0.083	0.054
0.181	0.933	0.137	0.568	0.560	0.636	0.863	0.067	0.071
0.18	0.933	0.118	0.559	0.554	0.600	0.882	0.067	0.051
0.173	0.950	0.118	0.568	0.559	0.667	0.882	0.050	0.068
0.157	0.950	0.098	0.559	0.553	0.625	0.902	0.050	0.048
0.154	0.967	0.098	0.568	0.558	0.714	0.902	0.033	0.065
0.112	0.967	0.078	0.559	0.552	0.667	0.922	0.033	0.045
0.110	0.967	0.059	0.550	0.547	0.600	0.941	0.033	0.025
0.076	0.967	0.039	0.541	0.542	0.500	0.961	0.033	0.006
0.07	0.967	0.020	0.532	0.537	0.333	0.980	0.033	−0.014
0.065	0.967	0.000	0.523	0.532	0.000	1.000	0.033	−0.033
0.043	0.983	0.000	0.532	0.536	0.000	1.000	0.017	−0.017
0.016	1.000	0.000	0.541	0.541		1.000	0.000	0.000
The best cut-off value and its associated metrics are colored. NPV – negative predictive value; FPR – false positive ratio, FNR – false negative ratio.

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Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis

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