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 2 Selected performance metrics (precision, recall, accuracy, AUC score) of the Random Forest Classifier estimators (trees) implemented on the testing subset (n=39).

tree ID	Precision	Recall	Accuracy	AUC
0	0.6111	0.4783	0.5128	0.6155
1	0.6000	0.5217	0.5128	0.5367
2	0.7222	0.5652	0.6154	0.6196
3	0.5714	0.3478	0.4615	0.4959
4	0.7368	0.6087	0.6410	0.6413
5	0.6667	0.2609	0.4872	0.5584
6	0.6316	0.5217	0.5385	0.5883
7	0.7143	0.4348	0.5641	0.6073
8	0.6667	0.6957	0.6154	0.6658
9	0.6471	0.4783	0.5385	0.5503
10	0.7143	0.6522	0.6410	0.6413
11	0.7391	0.7391	0.6923	0.7188
12	0.7059	0.5217	0.5897	0.6046
13	0.7000	0.3043	0.5128	0.5000
14	0.7778	0.3043	0.5385	0.5774
15	0.5000	0.3043	0.4103	0.4375
16	0.7692	0.4348	0.5897	0.6168
17	0.7778	0.3043	0.5385	0.5829
18	0.6538	0.7391	0.6154	0.5870
19	0.7059	0.5217	0.5897	0.5734
20	0.6154	0.6957	0.5641	0.5571
21	0.6842	0.5652	0.5897	0.6848
22	0.9167	0.4783	0.6667	0.7527
23	0.6500	0.5652	0.5641	0.5788
24	0.5556	0.2174	0.4359	0.5489
25	0.8235	0.6087	0.6923
26	0.5556	0.4348	0.4615	0.4511
27	0.6111	0.4783	0.5128	0.4715
28	0.8333	0.6522		0.6916
29	0.6250	0.4348	0.5128	0.5842
30	0.8000	0.5217	0.6410	0.6549
31	0.5882	0.4348	0.4872	0.4470
32	0.6500	0.5652	0.5641	0.5774
33	0.8235	0.6087	0.6923	0.7296
34	0.8571	0.5217	0.6667	0.6766
35	0.7368	0.6087	0.6410	0.6277
36	0.5263	0.4348	0.4359	0.4606
37	0.6875	0.4783	0.5641	0.5774
38	0.7059	0.5217	0.5897	0.6005
39	0.4444	0.1739	0.3846	0.3587
40	0.5500	0.4783	0.4615	0.4266
41	0.5714	0.5217	0.4872	0.4837
42	0.6875	0.4783	0.5641	0.6005
43	0.7037	0.8261	0.6923	0.6630
44	0.2941	0.2174	0.2308	0.2459
45	0.8182	0.3913	0.5897	0.5761
46	0.6667	0.3478	0.5128	0.5978
47	0.7273	0.3478	0.5385	0.6128
48	0.6316	0.5217	0.5385	0.5190
49	0.6667	0.2609	0.4872	0.6848
50	0.4615	0.2609	0.3846	0.5231
51	0.7619	0.6957	0.6923	0.7283
52	0.6429	0.3913	0.5128	0.4783
53	0.6923	0.3913	0.5385	0.5082
54	0.6667	0.4348	0.5385	0.6889
55	0.5385	0.3043	0.4359	0.4348
56	0.6087	0.6087	0.5385	0.5394
57	0.8000	0.5217	0.6410	0.7554
58	0.6087	0.6087	0.5385	0.5299
59	0.8235	0.6087	0.6923	0.6821
60	0.5217	0.5217	0.4359	0.4022
61	0.6400	0.6957	0.5897	0.5856
62	0.7500	0.2609	0.5128	0.7391
63	0.6333	0.8261	0.6154	0.5897
64	0.6087	0.6087	0.5385	0.5285
65	0.6800	0.7391	0.6410	0.6793
66	0.5000	0.5652	0.4103	0.3478
67	0.7143	0.4348	0.5641	0.5707
68	0.5714	0.6957	0.5128	0.4565
69	0.6471	0.4783	0.5385	0.5435
70	0.6190	0.5652	0.5385	0.5258
71	0.7059	0.5217	0.5897	0.6264
72	0.6400	0.6957	0.5897	0.5666
73	0.6000	0.3913	0.4872	0.5231
74	0.5833	0.6087	0.5128	0.4375
75	0.5714	0.3478	0.4615	0.5679
76	0.5882	0.4348	0.4872	0.5054
77	0.5556	0.2174	0.4359	0.4837
78	0.6154	0.3478	0.4872	0.4837
79	0.6667	0.4348	0.5385	0.5707
80	0.6500	0.5652	0.5641	0.5313
81	0.6667	0.6087	0.5897	0.6590
82	0.8182	0.3913	0.5897	0.5992
83	0.8000	0.3478	0.5641	0.6087
84	0.5455	0.2609	0.4359	0.4579
85	0.8889	0.3478	0.5897	0.6413
86	0.7368	0.6087	0.6410	0.7228
87	0.6111	0.4783	0.5128	0.5666
88	0.7692	0.4348	0.5897	0.6766
89	0.7000	0.6087	0.6154	0.6073
90	0.6923	0.3913	0.5385	0.5774
91	0.5714	0.1739	0.4359	0.5285
92	0.6923	0.7826	0.6667	0.6196
93	0.7143	0.2174	0.4872	0.6603
94	0.6522	0.6522	0.5897	0.5448
95	1.0000	0.1304	0.4872	0.6685
96	0.8125	0.5652	0.6667	0.7120
97	0.5789	0.4783	0.4872	0.4891
98	0.6250	0.4348	0.5128	0.5231
99	0.5000	0.3043	0.4103	0.3981
Rows describing the two trees of best accuracy or AUC score are white.

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

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