Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: a study on Parkinson�s disease

Show simple item record

dc.contributor.author Vadera, Meet et al.
dc.date.accessioned 2020-05-15T12:42:39Z
dc.date.available 2020-05-15T12:42:39Z
dc.date.issued 2016-08
dc.identifier.citation Vadera, Meet et al., "Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: a study on Parkinson�s disease", IFAC-PapersOnLine, DOI: 10.1016/j.ifacol.2016.07.331, vol. 49, no. 7, pp. 990-995, Aug. 2016. en_US
dc.identifier.issn 2405-8963
dc.identifier.uri http://dx.doi.org/10.1016/j.ifacol.2016.07.331
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5396
dc.description.abstract A new era of intelligent medical diagnostics is emerging with the development of machine learning-based algorithms to diagnose neurodegenerative diseases (NDDs). In the present work, we discuss an innovative framework that uses principal component analysis (PCA) for feature extraction, Fisher discriminant ratio (FDR) for feature selection and support vector machines (SVM) for classification of Healthy controls, Parkinson�s Disease and SWEDD subjects. We have extended our framework to handle the challenge of multi-class disease diagnosis, wherein, accuracy up to 100% has been achieved. This demonstrates the potential of the present methodology to be developed into a clinical relevant diagnostic and decision support system.
dc.description.statementofresponsibility by Meet Vadera et al.
dc.format.extent vol. 49, no. 7, pp. 990-995
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Classification en_US
dc.subject Computer-aided diagnosis en_US
dc.subject Decision Support system en_US
dc.subject Image-processing en_US
dc.subject Knowledge based systems en_US
dc.subject Machine learning en_US
dc.title Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: a study on Parkinson�s disease en_US
dc.type Article en_US
dc.relation.journal IFAC-PapersOnLine


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search Digital Repository


Browse

My Account