ArticleJournal of neural transmission (Vienna, Austria : 1996)2012
Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.
Article in Journal of neural transmission (Vienna, Austria : 1996), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 4 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Clinical prediction models using artificial intelligence approaches in dementia.Aging clinical and experimental research · 2025Pooled it
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI).The Cochrane database of systematic reviews · 2021Pooled it
- Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI).The Cochrane database of systematic reviews · 2015Pooled it
- Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.International journal of molecular sciences · 2019Article
- Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.BMC medical informatics and decision making · 2017Article
- Alzheimer's disease--subcortical vascular disease spectrum in a hospital-based setting: Overview of results from the Gothenburg MCI and dementia studies.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2016Review
- Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers.BMC bioinformatics · 2014Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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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