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. 2020 Apr 5;12(1):e12028. doi: 10.1002/dad2.12028

FIGURE 3.

FIGURE 3

Classification performance using putative metabolites. Receiver operating characteristic area under the curve (ROC AUC) for the classification models using the nine unidentified features for the cognitively unaffected and stable (CS) versus Down syndrome‐Alzheimer's disease (DS‐AD) comparison (A) and the five features for Down syndrome‐mild cognitive impairment (DS‐MCI) versus DS‐AD comparison (B). For the CS versus DS‐AD comparison, the left panel shows strong classification using a logistic regression model with 10‐fold cross validation (ROC AUC = 0.868), the middle panel shows similar performance for the same model using a more rigorous 100‐fold Monte Carlo cross validation procedure (ROC AUC = 0.855), and the right panel shows consistent classification performance using an alternate support vector machine (SVM) classification algorithm (ROC AUC = 0.859). In the DS‐AD versus DS‐MCI comparison (B) the left panel shows strong classification performance using the logistic regression model with 10‐fold cross validation (ROC AUC = 0.891), the middle panel shows similar performance with 10‐fold Monte Carlo resampling approach (ROC AUC = 0.881) and the right panel shows strong SVM performance (ROC AUC = 0.885)