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Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.

Journal article
Authors Marcin Kruczyk
Henrik Zetterberg
Oskar Hansson
Sindre Rolstad
Lennart Minthon
Anders Wallin
Kaj Blennow
Jan Komorowski
Mats Gunnar Andersson
Published in Journal of neural transmission (Vienna, Austria : 1996)
Volume 119
Issue 7
Pages 821-31
ISSN 1435-1463
Publication year 2012
Published at Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Pages 821-31
Language en
Links dx.doi.org/10.1007/s00702-012-0812-...
Keywords Aged, Alzheimer Disease, cerebrospinal fluid, diagnosis, psychology, Amyloid beta-Peptides, cerebrospinal fluid, Disease Progression, Female, Humans, Male, Mild Cognitive Impairment, cerebrospinal fluid, psychology, Monte Carlo Method, Neuropsychological Tests, Peptide Fragments, cerebrospinal fluid, Phosphorylation, Predictive Value of Tests, tau Proteins, cerebrospinal fluid
Subject categories Neurochemistry

Abstract

The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid β (Aβ42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.

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