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

Journal of Neural Transmission
Marcin KruczykMats Gunnar Andersson

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 CS...Continue Reading

References

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Citations

Jul 30, 2015·Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism·Anders WallinMaria Bjerke
May 6, 2019·International Journal of Molecular Sciences·Xiaoyong PanYu-Dong Cai
Jul 28, 2021·The Cochrane Database of Systematic Reviews·Ingrid Arevalo-RodriguezSarah Cullum

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