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Behavioural Neurology
Volume 2017 (2017), Article ID 1850909, 19 pages
https://doi.org/10.1155/2017/1850909
Research Article

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Milano, Italy

Correspondence should be addressed to Isabella Castiglioni; ti.rnc.mfbi@inoilgitsac.allebasi

Received 27 May 2016; Revised 7 December 2016; Accepted 21 December 2016; Published 31 January 2017

Academic Editor: Michael Nitsche

Copyright © 2017 Petronilla Battista et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Subjects with Alzheimer’s disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions. However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist. Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment. We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR). Data were obtained from the ADNI database. In the groups of measures used as features, we included measures of both cognitive domains and subdomains. Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.