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Table 4 Comparative analysis with other conventional classifiers indicated by the area under the receiver operator curve (AUC) via five-cross validation on train dataset

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

   Proposed DNNs XGBoost AdaBoost Random Forest Bagging Support Vector Machine Logistic Regression
MMSE mean 0.9702 0.9605 0.9573 0.9581 0.9631 0.9627 0.9642
  std 0.0144 0.0144 0.0171 0.0192 0.0169 0.0196 0.0171
KLOSCAD-N mean 0.9863 0.9850 0.9774 0.9762 0.9724 0.9744 0.9807
  std 0.0048 0.0065 0.0107 0.0079 0.0069 0.0093 0.0080