Multi-class diagnosis of Neurodegenerative diseases: a Neuroimaging machine learning based approach

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dc.contributor.author Singh, Gurpreet
dc.contributor.author Vadera, Meet
dc.contributor.author Samavedham, Lakshminarayanan
dc.contributor.author Lim, Erle Chuen-Hian
dc.date.accessioned 2019-06-19T11:12:57Z
dc.date.available 2019-06-19T11:12:57Z
dc.date.issued 2019-05
dc.identifier.citation Singh, Gurpreet; Vadera, Meet; Samavedham, Lakshminarayanan and Lim, Erle Chuen Hian, "Multi-class diagnosis of Neurodegenerative diseases: a Neuroimaging machine learning based approach", Industrial & Engineering Chemistry Research, DOI: 10.1021/acs.iecr.8b06064, May 2019. en_US
dc.identifier.issn 0888-5885
dc.identifier.issn 1520-5045
dc.identifier.uri https://doi.org/10.1021/acs.iecr.8b06064
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4548
dc.description.abstract 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.
dc.description.statementofresponsibility by Gurpreet Singh, Meet Vadera, Lakshminarayanan Samavedham and Erle Chuen Hian Lim
dc.language.iso en en_US
dc.publisher American Chemical Society en_US
dc.title Multi-class diagnosis of Neurodegenerative diseases: a Neuroimaging machine learning based approach en_US
dc.type Article en_US
dc.relation.journal Industrial & Engineering Chemistry Research


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