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




diagnosis Alzheimer’s disease, ensemble learning methods, classification, convolutional neural network


In 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.

Author Biographies

Maryam Naderan, ESC "IASA" NTUU "Igor Sikorsky KPI", Kyiv

Naderan Maryam,

Ph.D., a student at ESC "IASA" NTUU "Igor Sikorsky KPI", Kyiv, Ukraine.

Yuriy P. Zaychenko, ESC "IASA" NTUU "Igor Sikorsky KPI", Kyiv

Yuriy Zaychenko,

Doctor of Technical Sciences, a professor at ESC "IASA" NTUU "Igor Sikorsky KPI", Kyiv, Ukraine.


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Progressive information technologies, high-efficiency computer systems