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A Comparative Analysis of the Ensemble Method for Liver Disease Prediction
BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh.
Department of Computer Science and Engineering, BGC Trust University, Bangladesh.
Department of Computer Science and Engineering, BGC Trust University, Bangladesh.
Department of Computer Science and Engineering, BGC Trust University, Bangladesh.
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2019 (English)In: Proceedings of International Conference on Innovation in Engineering and Technology (ICIET), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Early diagnosis of liver disease is very important in order to save human lives and take appropriate measure to control the disease. In several fields, especially in the field of medical science, the ensemble method was successfully applied. This research work uses different ensemble methods to investigate the early detection of liver disease. The selected dataset for this analysis is made up of attributes such as total bilirubin, direct bilirubin, age, sex, total protein, albumin, and globulin ratio. This research mainly aims at measuring and comparing the efficiency of different ensemble methods. AdaBoost, LogitBoost, BeggRep, BeggJ48 and Random Forest are the ensemble method used in this research. The study shows that LogitBoost is the most accurate model than other ensemble approaches.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Data Mining, Ensemble Method, Bagging, Boosting, Stacking, Liver Disease
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-76857OAI: oai:DiVA.org:ltu-76857DiVA, id: diva2:1372961
Conference
International Conference on Innovation in Engineering and Technology (ICIET)
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-12-06

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fulltext(325 kB)14 downloads
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CiteExportLink to record
Permanent link

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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
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