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Table 3 Supervised learning classification using three different algorithms: k-nearest neighbors, support vector machine, and naïve Bayes classificationa

From: EEG complexity as a biomarker for autism spectrum disorder risk

   Age
Population   6 months 9 months 12 months 18 months 24 months
  k-NN 0.67
(0.06)
0.77
(0.02)
0.53
(0.38)
0.72
(0.12)
0.53
(0.47)
All infants Accuracy (P value) SVM 0.63
(0.16)
0.77
(0.00)
0.53
(0.71)
0.65
(0.56)
0.55
(0.64)
  Bayes 0.70
(0.05)
0.72
(0.03)
0.68
(0.06)
0.80
(0.04)
0.57
(0.33)
  k-NN 0.40
(0.64)
0.90
(0.00)
0.70
(0.16)
0.90
(0.03)
-
Boys Accuracy (P value) SVM 0.30
(0.42)
1.00
(0.00)
0.75
(0.12)
0.75
(0.81)
-
  Bayes 0.35
(0.58)
0.75
(0.10)
0.75
(0.09)
0.90
(0.05)
-
  k-NN 0.80
(0.03)
0.60
(0.20)
0.48
(0.58)
0.35
(0.88)
0.40
(0.89)
Girls Accuracy (P value) SVM 0.80
(0.02)
0.40
(0.54)
0.35
(0.97)
0.55
(0.78)
0.75
(0.53)
  Bayes 0.75
(0.07)
0.65
(0.19)
0.47
(0.54)
0.45
(0.73)
0.50
(0.92)
  1. aTenfold cross-validation was run using the computed mean mMSE values on 64 channels for each infant within each age group. P values were estimated empirically using a permutation of class labels approach as described in the methods section under 'classification and endophenotypes. Identical cross-validation calculations with 100 permutations were performed to determine empirical P values with three different populations: all infants, boys only and girls only. Too few 24-month-old boys were available for cross-validation. k-NN, k-nearest neighbors algorithm; SVM, support vector machine algorithm; Bayes, naïve Bayes classification algorithm. Boldface entries highlight values with statistical significance of p < 0.05.