Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
AGGREGATION OF HETEROGENEOUS DATA IN ATHEROSCLEROSIS ASSESSMENT
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

‘The cardiovascular disease atherosclerosis can lead to life threatening conditions such as stroke. Th‘is study explores supervised learning to assess atherosclerosis in individuals, using heterogeneous data in the form of tabular data from several data sets and images. Aggregation of the data by utilizing data fusion, pre-processing techniques, di‚fferent learning methods and di‚fferent decision fusion approaches were explored in order to propose an architecture with focus on high accuracy on assessing atherosclerosis. Using a support vector machine for a concatenation of pre-processed tabular data, and a convolutional neural network for a pre-processed image, weighted majority voting on these models’ intermediatepredictions to produce a €final prediction yielded an accuracy of 89:20%. A generalized version of this architecture, which can address the task of classifi€cation with similar heterogeneous data, is also introduced. Both the architecture and the generalized version of the architecture di‚ffer from traditional methods o faddressing similar assessment tasks, which only considers homogeneous data.

Place, publisher, year, edition, pages
2019. , p. 50
Series
UMNAD ; 1201
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-164716OAI: oai:DiVA.org:umu-164716DiVA, id: diva2:1366340
External cooperation
Norrlands Universitetssjukhus
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-10-29Bibliographically approved

Open Access in DiVA

fulltext(913 kB)14 downloads
File information
File name FULLTEXT01.pdfFile size 913 kBChecksum SHA-512
cabb713ef712e50fe5d4c467bd7c876d5d671f01cad48ddc69b297911792eea7f1890c0a2c72e24c4c2784a0ea6a6b83177415ed06e102bdc14daa225aa8d2ba
Type fulltextMimetype application/pdf

By organisation
Department of Computing Science
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 14 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 123 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf