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Table 3 Main Features of Semeion Machine Learning Systems employed in the study

From: Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data

ANNs Architecture

Hidden layers

Learning Rate

Epochs

Initialization

Output Function

Nickname

Conic Net [31]

5 Layers=

4x12x12x12

0.01

1000

Auto-encoders

Soft Max

D_FF_Conic (4x12x12x12)

 

5 Layers=

6x12x12x12

0.01

1000

Auto-encoders

Soft Max

D_FF_Conic (6x12x12x12)

Sine Net [32,33,34]

1 Layer = 48

0.1

2000

Random

Soft Max

D_FF_Sn [35]

Back Propagation [29]

0 Layer = L

0.1

1000

Random

Soft Max

D_FF_Bp (0)

 

1 Layer = 24

0.1

1000

Random

Soft Max

D_FF_Bp

[22]

 

5 Layers=

16x16x16x16

0.01

2000

Auto-encoders

Soft Max

D_FF_Bp

(16x16x16x16)

Bi-Modal Net

1 Layer = 48

0.1

1000

Random

Soft Max

D_FF_Bm

[35]

Gauss Net [36]

1 Layer = 64

0.01

1000

Random

Soft Max

D_FF_GNet

[37]