Recurrent Neural Networks in Computer-Based Clinical Decision Support for Laryngopathies: An Experimental Study

Table 1

Selected results of experiments for women obtained using the Elman network.

PatientID	Original signal		Ddifferentiated signal
	𝐸𝐸𝑁	𝑛𝐸𝑁	𝐸𝐸𝑁	𝑛𝐸𝑁
𝑤^CG1	0.0068	389	0.0245	455
𝑤^CG2	2.4523	335	0.0208	650
𝑤^CG3	0.017	501	0.0341	497
𝑤^CG4	0.0109	597	0.01	422
𝑤^CG5	0.0332	662	0.0566	650
𝑤^CG6	0.0178	609	0.0324	656
𝑤^CG7	0.0096	428	0.0202	333
𝑤^CG8	0.0068	318	0.028	575
𝑤^CG9	0.008	490	0.0216	925
𝑤^CG10	0.0084	553	0.05	504
𝑤^LP1	0.172	331	0.1081	564
𝑤^LP2	0.2764	536	0.1936	622
𝑤^LP3	0.0518	566	0.0533	593
𝑤^LP4	0.0268	504	0.0879	498
𝑤^LP5	0.0418	646	0.1726	547
𝑤^LP6	0.2107	444	0.2468	506
𝑤^LP7	0.0921	1040	0.1687	439
𝑤^LP8	0.0364	992	0.1396	758
𝑤^LP9	0.038	541	0.1061	826
𝑤^LP10	0.1461	363	0.2448	711
𝑤^RE1	0.039	360	0.055	487
𝑤^RE2	0.1006	452	0.1	729
𝑤^RE3	0.1021	446	0.1583	608
𝑤^RE4	0.0636	780	0.0804	586
𝑤^RE5	0.1626	446	0.2376	545
𝑤^RE6	0.1953	477	0.1905	500
𝑤^RE7	0.2027	337	0.1661	378
𝑤^RE8	0.1927	457	0.1367	717
𝑤^RE9	0.2908	939	0.2139	865
𝑤^RE10	0.4357	679	0.3795	820