Table 4 Confusion matrix for the SVM algorithm with concatenated metrics (local dataset): confusion matrices that show the performance of the GA, SFFS, NSGA-II, PSO algorithms when used with the SVM classifier.

From: Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment

GA

SFFS

NSGA-II

PSO

Confusion matrix

Sensitivity

Specificity

Confusion matrix

Sensitivity

Specificity

Confusion matrix

Sensitivity

Specificity

Confusion matrix

Sensitivity

Specificity

\(\left[\begin{array}{cc}43& 3\\ 8& 24\end{array}\right]\)

93%

75%

\(\left[\begin{array}{cc}45& 1\\ 9& 23\end{array}\right]\)

97%

72%

\(\left[\begin{array}{cc}42& 4\\ 6& 26\end{array}\right]\)

91%

81%

\(\left[\begin{array}{cc}42& 4\\ 8& 24\end{array}\right]\)

95%

75%

  1. The table also shows the sensitivity and specificity values for each depicted algorithm.
  2. GA genetic algorithm, SFFS sequential floating forward selection, NSGA-II non-dominated sorting genetic algorithm II, PSO particle swarm optimization, SVM support vector machine.
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