Table 3 

Supervised learning classification using three different algorithms: knearest neighbors, support vector machine, and naïve Bayes classification^{a} 

Age 



Population 
6 months 
9 months 
12 months 
18 months 
24 months 



kNN 
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) 

kNN 
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) 
 

kNN 
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) 



^{a}Tenfold crossvalidation 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 crossvalidation calculations with 100 permutations were performed to determine empirical P values with three different populations: all infants, boys only and girls only. Too few 24monthold boys were available for crossvalidation. kNN, knearest neighbors algorithm; SVM, support vector machine algorithm; Bayes, naïve Bayes classification algorithm. Boldface entries highlight values with statistical significance of p < 0.05. 

Bosl et al. BMC Medicine 2011 9:18 doi:10.1186/17417015918 