Artificial neural networks in the discrimination of Alzheimer's disease using biomarkers data

Abstract

This paper presents the results of a study developing artificial neural network system (ANN) for classification of Alzheimer's disease (AD) and healthy patients. The classification is done using biomarkers, from cerebrospinal fluid: albumin ratio (CSF/Serum and/or Plasma), Aβ40 (CSF), Aβ42 (CSF), tau-total (CSF) and tau-phospho (CSF). Neural network input parameters are datasets from Alzbiomarkers database. Independent t-test is used to calculate statistical difference between input parameters. Developed neural network was validated with 80 subjects from Alzbiomarkers database. Out of 45 AD subjects, 43 were correctly classified as AD patients, obtaining a sensitivity of 95.5%, and out of 35 healthy subjects 32 were correctly classified obtaining specificity of 91.43%.