Methods for improving accuracy of the dementia diagnosis using feature dimension reduction

dc.contributor.authorNaderan, Maryam
dc.contributor.authorZaychenko, Yuriy
dc.date.accessioned2022-05-19T04:38:22Z
dc.date.available2022-05-19T04:38:22Z
dc.date.issued2019
dc.description.abstractenIn this paper, the problem of choosing the right feature for diagnosing Dementia is discussed. Several features that could affect dementia were reviewed and their importance was evaluated. Random forest algorithm and SVM for the dementia diagnosis have been developed and investigated. Experiments were conducted on the open-source database and compared with the related works’ results. The purpose of the paper is to improve the accuracy of diagnosis of dementia using the reduction of features' dimension. This article is devoted to analysis of the main distinguishing features of Alzheimer`s dementia, applicable methods and treatment of Alzheimer's dementia on early stage that could help to avoid negative consequences connected with progress of the disease. The purpose of the paper is to improve the accuracy of diagnosis of dementia.uk
dc.format.pagerangeС. 25-30uk
dc.identifier.citationNaderan, M. Methods for improving accuracy of the dementia diagnosis using feature dimension reduction / Maryam Naderan, Yuriy Zaychenko // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2019. – № 2. – С. 25-30. – Бібліогр.: 7 назв.uk
dc.identifier.doihttps://doi.org/10.20535/SRIT.2308-8893.2019.2.03
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/47439
dc.language.isoenuk
dc.publisherКПІ ім. Ігоря Сікорськогоuk
dc.publisher.placeКиївuk
dc.sourceСистемні дослідження та інформаційні технології, № 4uk
dc.subjectdiagnosis Alzheimer’s diseaseuk
dc.subjectensemble learning methodsuk
dc.subjectclassificationuk
dc.subjectconvolutional neural networkuk
dc.subject.udc004.855.5uk
dc.titleMethods for improving accuracy of the dementia diagnosis using feature dimension reductionuk
dc.typeArticleuk

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