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Table 2 Detection precision for ovarian cancer survival predictions

From: Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks

NTI

P (%)

LSTM CNN [3]

P (%)

DLNN [8]

P (%)

DBN [14]

P (%)

This Work

300

82.56

84.89

81.26

87.90

600

80.06

80.61

77.86

87.56

900

81.48

79.06

80.29

92.03

1200

81.78

82.39

78.61

90.38

1350

83.96

82.58

80.79

92.59

1650

87.06

85.77

79.94

93.75

1950

84.09

82.99

86.06

87.89

2250

88.13

82.25

86.20

95.05

2550

84.16

84.59

81.38

94.26

2850

84.23

88.88

85.56

90.46

3300

87.37

85.15

80.75

93.67

3750

82.53

84.41

83.96

94.89

4200

81.71

83.67

85.17

89.14

4650

85.86

87.94

84.38

91.37

5100

85.00

89.21

83.58

89.59

5700

85.11

85.50

86.77

92.79

6150

89.22

83.77

88.96

98.00

6600

85.32

85.05

88.14

91.21

7050

84.43

87.33

89.33

93.41

7500

88.55

85.61

89.53

96.63

7950

82.67

89.90

83.73

95.86

8400

83.80

91.19

84.94

94.08

8850

82.93

89.47

90.13

96.31

9450

90.05

88.76

84.34

94.54

  1. The performance metrics in Table 2 indicate that the model’s accuracy, precision, recall, and F1 score improve as the NTI increases from 7050 to 7500. However, beyond 7500 NTIs, the performance metrics show no significant improvement. This trend can be attributed to several factors:
  2. Convergence of Model Parameters: