Fig. 3

Machine learning models to differentiate MCI and AD, and identify high-risk P-MCI patients. A The top differentiating features ranked by selection frequency are shown for three different classification models: (i) P-MCI vs. S-MCI; (ii) AD vs. S-MCI; (iii) AD vs. P-MCI. B PCA score plot of all 174 features from joint selected genes of all three classifications. C ROC curves of three groups of classifiers: (i) P-MCI vs. S-MCI; (ii) AD vs. P-MCI; (iii) AD vs. S-MCI using optimal number of selected features by five-fold cross validation. The 95% confidence interval (CI) values are also indicated. D Performance assessment of all three classifiers: (i) P-MCI vs. S-MCI; (ii) AD vs. S-MCI; (iii) AD vs. P-MCI using the top selected features. For each classifier, results are shown for all subjects combined (♀ + ♂), for females only (♀), and for males only (♂). The results are presented as means. MCI, mild cognitive impairment; P-MCI, Progressive MCI; S-MCI, Stable MCI