Table 6 Classification accuracy and computational efficiency of SGA-RF.

From: SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study

Optimization Algorithm

Classifier

No. of Selected Features

Accuracy (%)

Precision (%)

Recall (%)

F1-Score (%)

Average Runtime (seconds)

Genetic Algorithm (GA)

RF

28

96.45

95.21

96.01

95.61

12.34

Particle Swarm Optimization (PSO)

RF

25

97.32

96.88

97.1

96.99

10.72

Seagull Optimization Algorithm (SGA)

RF

22

99.01

98.88

99

98.94

3.25

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