Implementation of an artificial neural network on the test Barcelona workstation as a predictive model for the classification of normal, mild cognitive impairment and Alzheimer's disease subjects using the Neuronorma battery

Objective: to implement an online Artificial Neural Network (ANN) that provides the probability of a subject having mild cognitive impairment(MCI) or Alzheimer’s disease (AD). Method: Different ANN’s were trained with a sample of 350 controls, 75 MCI and 93 AD subjects. The ANN structure chosen was: (1) an input layer of 33 cognitive variables from the NEURONOR-MA battery plus two sociodemographic variables, age and education, which was reduced to a 15-fea-ture input vector using the Multiple Discriminant Analysis method; (2) one hidden layer with 8 neurons; and (3) three outputs corresponding to the 3cognitive states. This ANN was determinate in a previous study. The ANN was implemented in TestBarcelona Workstation.189 Results: The best designed ANN diagnoses with a probability of 94.87% subjects well classified when comparing controls, MCI and AD using the NEURONORMA battery. Conclusions: ANNs are a powerful tool for classifying subjects with cognitive impairment using the NEURONORMA battery. When a single profile is entered, it delivers the probabilities of be-longing to each one of the three cognitive states. This constitutes a good complement to the interpretation of neuropsychological profiles for clinical decision making.Peer Reviewe