Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention
of dementia; however, automated detection of early cognitive deterioration is an unresolved
issue. The aim of our study was to compare various classification approaches to differentiate
MCI patients from healthy controls, based on rs-fMRI data, using machine learning
(ML) algorithms. Own dataset (from two centers) and ADNI database were used during
the analysis. Three fMRI parameters were applied in five feature selection algorithms:
local correlation, intrinsic connectivity, and fractional amplitude of low frequency
fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied
for classification. We achieved a relatively wide range of 78-87% accuracy for the
various feature selection methods with SVM combining the three rs-fMRI parameters.
In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a
more harmonized result among the feature selection algorithms in both datasets with
80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower
performance metrics of some algorithms, most of the results were positive and could
be seen in two unrelated datasets which increase the validity of our methods. Our
results highlight the potential of ML-based fMRI applications for automated diagnostic
techniques to recognize MCI patients.
