Sharma, Ankita and Shukla, Deepika and Goel, Tripti and Mandal, Pravat K (2019) Bharat: An Integrated Big Data Analytic Model for Early Diagnostic Biomarker of Alzheimer's Disease. Front Neurol., 10 (9).

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Abstract

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. Progressive and relentless efforts are being made for therapeutic development by way of advancing understanding of non-invasive imaging modalities for the causal molecular process of AD. We present a Hadoop-based big data framework integrating non-invasive magnetic resonance imaging (MRI), MR spectroscopy (MRS) as well as neuropsychological test outcomes to identify early diagnostic biomarkers of AD. This big data framework for AD incorporates the three “V”s (volume, variety, velocity) with advanced data mining, machine learning, and statistical modeling algorithms. A large volume of longitudinal information from non-invasive imaging modalities with colligated parametric variety and speed for both data acquisition and processing as velocity complete the fundamental requirements of this big data framework for early AD diagnosis. Brain structural, neurochemical, and behavioral features are extracted from MRI, MRS, and neuropsychological scores, respectively. Subsequently, feature selection and ensemble-based classification are proposed and their outputs are fused based on the combination rule for final accurate classification and validation from clinicians. A multi-modality-based decision framework (BHARAT) for classification of early AD will be immensely helpful.

Item Type:	Article
Subjects:	Neurodegenerative Disorders Neuro-Oncological Disorders Neurocognitive Processes Neuronal Development and Regeneration Informatics and Imaging Genetics and Molecular Biology
Depositing User:	Dr. D.D. Lal
Date Deposited:	17 Jun 2019 07:24
Last Modified:	13 Dec 2021 09:48
URI:	http://nbrc.sciencecentral.in/id/eprint/495

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