Table 2 Classification performance of the optimal model including different input features using the integrated machine learning framework (tenfold).

From: An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data

Input feature

Feature Selection Method

Classifier

Accuracy (%)

Sensitivity (%)

Specificity (%)

AUC

p valuea

Gut microbiota features (n = 77)

RFE

RF

70.8

58.3

83.3

0.80

0.03

Blood features (n = 12)

Noneb

KNN

83.3

83.3

83.3

0.88

0.010

EEG features (n = 574)

RFE

RF

79.2

83.3

75.0

0.90

0.010

Combined features (n = 663)

None

SVM

91.7

91.7

91.7

0.97

0.010

  1. AUC area under the receiver operating characteristic curve, RFE recursive feature elimination, KNN k-nearest neighbor, LR logistic regression, RF random forest, SVM support vector machine, EEG electroencephalogram.
  2. aThe statistical significance of the permutation test was set to p < 0.05.
  3. bNone means no feature selection algorithm was used.
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