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Table 6 Table summarizes the accuracy of ANN, GRU, BILSTM, LSTM, and 1DCNN model for 3 SNPs

From: Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Model, SNPs = 3

Activation

Dropout

Optimizer

Batchsize

Epochs

Validation

Accuracy

ANN

Sigmoid

0.2

Adam

1

10

0.2

0.88

Sigmoid

0.2

Adam

10

20

0.4

0.89

Relu

0.3

RMSprop

15

50

0.3

0.89

Relu

0.3

SGD

1

50

0.2

0.89

GRU

Sigmoid

0.2

Adam

1

10

0.2

0.895

Sigmoid

0.2

RMSprop

10

50

0.3

0.895

BILTM

Sigmoid

0.2

Adam

1

10

0.2

0.895

Sigmoid

0.3

RMSprop

10

100

0.3

0.895

LSTM

Sigmoid

0.2

Adam

1

10

0.2

0.9

1DCNN

Sigmoid

0.2

Adam

1

20

0.2

0.88

Sigmoid

0.2

Adam

1

50

0.2

0.89

Softmax

0.3

RMSprop

20

100

0.3

0.89

Softmax

0.2

Adam

20

50

0.2

0.89

Relu

0.3

RMSprop

15

50

0.4

0.89

  1. LSTM performs well with an accuracy of 0.9%