6-Protein Signature Classifier Dataset size PAM SMO Simple Logistic Logistic Multilayer Perceptron* Bayes Net Naı¨ve Bayes Naı¨ve Bayes Simple Naı¨ve Bayes Up IB1 Ibk Kstar LWL AdaBoost ClassViaRegression Decorate* MultiClass Classifier Random Committee* j48 LMT NBTree Part Random Forest* Ordinal Classifier Average Agreement (%) 5-Protein Signature Classifier Dataset size PAM SMO Simple Logistic Logistic Multilayer Perceptron* Bayes Net Naı¨ve Bayes Naı¨ve Bayes Simple Naı¨ve Bayes Up PLoS ONE | www.plosone.org 139 20 20 18 21 25.6 22 23 24 23 33 33 33 29 27 23 24.7 21 26.6 24 18 21 27 25.6 24 24.44 82% 139 21 19 18 20 21.6 21 19 20 19 64 8 9 4 4 3.2 8 8 9 8 9 9 6 16 11 10 9.8 4 11.5 10 4 10 13 11.8 10 8.60 87% 64 10 8 4 4 5.3 4 5 5 5 75 12 11 14 17 22.4 14 15 15 15 24 24 27 13 16 13 14.9 17 15.1 14 14 11 14 13.8 14 15.84 79% 75 11 11 14 16 16.3 17 14 15 14 7 Table 7. Report of the results of the 24 classifiers when using the 6-Protein biomarker. Alzheimer Grand Total OVERALL (‘‘AD’’+‘‘MCI’’) AD Er. NAD Er. Test Set ‘‘AD’’ AD Er. Test Set ‘‘MCI’’ NAD Er. AD Er. NAD Er. Using this biomarker it is notable the effectiveness of predicting AD on the ‘‘AD’’ test set using simple classifiers as simple logistic or LMT (Logistic Model Tree) or even the same classifier used in [1] (PAM). doi:10.1371/journal.pone.0003111.t007 Table 8. Report of the results of the 24 classifiers when using the 5-Protein biomarker. Grand Total OVERALL (‘‘AD’’+‘‘MCI’’) AD Er. NAD Er. Test Set ‘‘AD’’ AD Er. Test Set ‘‘MCI’’ NAD Er. AD Er. NAD Er. September 2008 | Volume 3 | Issue 9 | e3111 42 1 2 0 0 0.4 3 2 3 2 3 3 1 6 3 3 2.4 0 3.1 2 0 1 3 2.6 2 2.02 95% 42 3 2 0 0 0.7 1 1 1 1 50 3 2 4 7 9 4 5 5 5 11 11 13 3 6 6 4.8 7 5.6 5 4 2 5 4.4 5 5.70 89% 50 2 2 4 6 5.2 5 2 3 2 22 7 7 4 4 2.8 5 6 6 6 6 6 5 10 8 7 7.4 4 8.4 8 4 9 10 9.2 8 6.58 70% 22 7 6 4 4 4.6 3 4 4 4 25 9 9 10 10 13.4 10 10 10 10 13 13 14 10 10 7 10.1 10 9.5 9 10 9 9 9.4 9 10.14 59% 25 9 9 10 10 11.1 12 12 12 12