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Table 2 Classification performances on the imputed dataset indicated by the area under the receiver operator curve (AUC)

From: Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles

  Proposed DNNs XGBoost Logistic Regression Random Forest Adaboost Bagging Support Vector Machine
MinMax 0.9489 0.9506 0.9083 0.9405 0.9149 0.9334 0.8898
kNN 0.9603 0.9541 0.9356 0.9466 0.9444 0.9559 0.9321
MI 0.9586 0.9524 0.9312 0.9211 0.9184 0.9418 0.9347
LLS 0.9594 0.9471 0.9295 0.9343 0.9109 0.9339 0.9383
  1. MinMax: minimum-maximum imputation, kNN: k nearest neighbor imputation, MI: multiple imputation, LLS: local least square imputation