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Sequential analysis of serial measurements based on multivariate reference regions
M. P. Krivenko Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
Abstract:
Sequential data series analysis procedures are considered. An approach is developed when a set of multivariate features of a certain object, which varies in time, is presented as a single vector of observed values. By increasing the dimensionality of the data, it is possible to obtain a single picture of the description of objects, to take into account the objectively existing dependence of individual observations, and to simulate changes over time. The basis for solving classification problems is the use of multivariate reference regions. Three options for data processing procedures are proposed, their properties are investigated, and recommendations for practical application are developed. To demonstrate the capabilities of these procedures, the task of early diagnosis of cancer using the PSA (prostate-specific antigen) biomarker is considered. Features of the application of sequential methods for analyzing data series are indicated, recommendations for their effective use are formed, and the advantages of the consolidating approach in data analysis are identified.
Keywords:
serial measurements, consolidation approach, sequential procedures, analysis of prostate-specific antigen (PSA).
Received: 05.03.2020
Citation:
M. P. Krivenko, “Sequential analysis of serial measurements based on multivariate reference regions”, Inform. Primen., 14:2 (2020), 86–91
Linking options:
https://www.mathnet.ru/eng/ia666 https://www.mathnet.ru/eng/ia/v14/i2/p86
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Statistics & downloads: |
Abstract page: | 86 | Full-text PDF : | 61 | References: | 17 |
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