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

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• 作者：Marcin Kruczyk (123) kruczyk@gmail.com
  Henrik Zetterberg (45) henrik.zetterberg@clinchem.gu.se
  Oskar Hansson (67) oskar.hansson@med.lu.se
  Sindre Rolstad (4) sindre.rolstad@neuro.gu.se
  Lennart Minthon (67) lennart.minthon@skane.se
  Anders Wallin (4) anders.wallin@neuro.gu.se
  Kaj Blennow (4) kaj.blennow@neuro.gu.se
  Jan Komorowski (23) jan.komorowski@lcb.uu.se
  Mats Gunnar Andersson (8) gunnar.andersson@sva.se
• 关键词：Alzheimer’s disease – Decision support – Monte Carlo feature selection – Rosetta – Rough sets – Biomarkers – Cerebrospinal fluid
• 刊名：Journal of Neural Transmission
• 出版年：2012
• 出版时间：July 2012
• 年：2012
• 卷：119
• 期：7
• 页码：821-831
• 全文大小：502.7 KB
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• 作者单位：1. Postgraduate School for Molecular Medicine, ?wirki i Wigury 61 Street, 02-091 Warsaw, Poland2. Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics, Uppsala University, 751 24 Uppsala, Sweden3. Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University, 02-106 Warsaw, Poland4. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, M?lndal, Sweden5. UCL Institute of Neurology, London, WC1N 3BG UK6. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malm?, Sweden7. The Neuropsychiatric Clinic, Malm? University Hospital, Malm?, Sweden8. Department of Chemistry, Environment and Feed Hygiene, National Veterinary Institute (SVA), Uppsala, Sweden
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Neurology
  Pharmacology and Toxicology
  Psychiatry
  
• 出版者：Springer Wien
• ISSN：1435-1463

文摘

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-tau₁₈₁ (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.