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Table 2 XMITTN output for ADNI and Emory cohorts assessing whether classification using two sets of variables is better than chance

From: CSF complement 3 and factor H are staging biomarkers in Alzheimer’s disease

Machine learning algorithm p-value, Experiment 1, ADNI cohort p-value, Experiment 2, ADNI cohort p-value, Experiment 1, Emory cohort p-value, Experiment 2, Emory cohort
Logistic 0.510 0.140   
Perceptron 0.792 0.912   
Decision Tree 0.197 0.161   
Random Forests 0.128 0.043 0.560 0.595
Naïve Bayes 0.403 0.367   
K-Nearest Neighbor 0.106 0.069   
Boosted Decision Tree 0.399 0.245   
Gradient Boosting 0.185 0.104   
Support Vector Machine 0.125 0.033 0.266 0.014
  1. Experiment 1 includes 6 features: age, gender, presence of at least one APOE ε4 allele, CSF Aβ42, CSF t-Tau, and CSF p-Tau181, and no ML algorithm performed better than chance in distinguishing between the two AD stages. Experiment 2 has all previous features plus C3 and FH and levels, and achieved improved classification in two algorithms in the ADNI cohort and support vector machine in the Emory cohort (p < 0.05 shown in bold)