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Abstract #3498

Classification of Alzheimer's Disease Based on Amyloid-PET using Random Forest Ensemble

Yiwen Bao1, Patrick Ka-Chun Chiu2, Yat-Fung Shea2, Joseph SK Kwan3, Felix Hon Wai Chan2, and Henry Ka-Fung Mak1
1Department of diagnostic radiology, University of Hong Kong, Hong Kong, Hong Kong, 2Department of medicine, Queen Mary Hospital, Hong Kong, Hong Kong, 3Department of brain sciences, Imperial College London, London, United Kingdom

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.

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