Random forest model as a high efficacy classifier was incorporated in our study for supporting clinical diagnosis. We aimed at evaluating the accuracy of RF model in distinguishing HC, MCI from AD and the importance of various neuroradiological features in selection. Additionally, in order to unify quantitative amyloid uptake across three cohorts, we transformed SUVR into standard Centiloid unit. The results indicated that RF model had moderate to high accuracy in differentiating AD from HC and MCI. Regional Ab load had more important effects than other features in distinguishing AD from others.