Table 7 Confusion matrix for the RF algorithm for different rs-fMRImetrics (ADNI dataset): confusion matrices that show the performance of the GA, SFFS, algorithms when used with the RF classifier.

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

GA-LCOR

SFFS-LCOR

SFFS-concatenated metrics

Confusion matrix

Sensitivity

Specificity

Confusion matrix

Sensitivity

Specificity

Confusion matrix

Sensitivity

Specificity

\(\left[\begin{array}{cc}74& 12\\ 17& 52\end{array}\right]\)

86%

75%

\(\left[\begin{array}{cc}78& 1\\ 20& 49\end{array}\right]\)

90%

71%

\(\left[\begin{array}{cc}74& 12\\ 16& 53\end{array}\right]\)

95%

65%

  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, RF random forest.
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