Multiclass Diagnosis of Neurodegenerative Diseases: A Neuroimaging Machine-Learning-Based Approach

With the advent of
powerful analysis tools, intelligent medical
diagnostics for neurodegenerative disease (NDs) diagnosis are coming
close to becoming a reality. In this work, we describe a state-of-the-art
machine-learning system with multiclass diagnostic capabilities for
the diagnosis of NDs. Our framework for multiclass subject classification
comprises feature extraction using principal component analysis, feature
selection using Fisher discriminant ratio, and subject classification
using least-squares support vector machines. A multisite, multiscanner
data set containing 2540 patients clinically diagnosed as Alzheimer
Disease (AD), healthy controls (HC), Parkinson disease (PD), mild
cognitive impairment (MCI), and scans without evidence of dopaminergic
deficit (SWEDD) was obtained from Parkinson’s Progression Marker
Initiative and Alzheimer’s Disease Neuroimaging Initiative.
Our work assumes significance since studies have primarily focused
on comparing only two subject classes at once, i.e., as binary classes.
To profile the diagnostic capabilities for real-time clinical practice,
we tested our framework for multiclass disease diagnostic capabilities.
The proposed method has been trained and tested on this cohort (2540
subjects), the largest reported so far in the literature. For multiclass
diagnosis, our method results in highest reported classification accuracy
of 87.89 ± 03.98% with a precision of 82.54 ± 08.85%. Also,
we have obtained accuracy of up to 100% for binary class classification
of NDs. We believe that this study takes us one step closer to translating
machine learning into routine clinical settings as a decision support
system for ND diagnosis.