Table 2 Classifier outcome of DCFNN-SOCVDC model under dissimilar epochs.

From: Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services

Classes

\(Accu_{y}\)

\(Prec_{n}\)

\(Reca_{l}\)

\(F1_{score}\)

\(G_{Measure}\)

Epoch—500

 Absence-CVD

99.11

98.98

99.11

99.05

99.05

 Presence-CVD

98.98

99.11

98.98

99.04

99.04

 Average

99.05

99.05

99.05

99.04

99.05

Epoch—1000

 Absence-CVD

97.17

97.63

97.17

97.40

97.40

 Presence-CVD

97.64

97.18

97.64

97.41

97.41

 Average

97.40

97.40

97.40

97.40

97.40

Epoch—1500

 Absence-CVD

98.05

98.82

98.05

98.44

98.44

 Presence-CVD

98.83

98.07

98.83

98.45

98.45

 Average

98.44

98.45

98.44

98.44

98.44

Epoch—2000

 Absence-CVD

96.90

97.14

96.90

97.02

97.02

 Presence-CVD

97.15

96.91

97.15

97.03

97.03

 Average

97.02

97.02

97.02

97.02

97.02

Epoch—2500

 Absence-CVD

96.85

96.92

96.85

96.88

96.88

 Presence-CVD

96.92

96.85

96.92

96.89

96.89

 Average

96.88

96.89

96.88

96.88

96.89

Epoch—3000

 Absence-CVD

97.73

98.35

97.73

98.04

98.04

 Presence-CVD

98.36

97.74

98.36

98.05

98.05

 Average

98.04

98.04

98.04

98.04

98.04