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.