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:...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/125203 |
| Acceso en línea: | https://hdl.handle.net/2117/125203 https://dx.doi.org/10.18502/kls.v4i8.3334 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Neural networks (Computer science) Neural networks Cognitive impairment Alzheimer’s disease NEURONORMA battery Aprenentatge automàtic Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | 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. |
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