A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

2nd International Conferenceon Innovation in Engineering andTechnology

Document identifier: oai:DiVA.org:ltu-76857
Access full text here:10.1109/ICIET48527.2019.9290507
Keyword: Natural Sciences, Computer and Information Sciences, Media and Communication Technology, Naturvetenskap, Data- och informationsvetenskap, Medieteknik, Data Mining, Ensemble Method, Bagging, Boosting, Stacking, Liver Disease, Pervasive Mobile Computing, Distribuerade datorsystem
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communities
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Nazmun Nahar

BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh
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Ferdous Ara

Department of Computer Science and Engineering, BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh
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Md. Arif Istiek Neloy

Department of Computer Science and Engineering and Engineering, BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh
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Vicky Barua

Department of Computer Science and Engineering and Engineering, BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh
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Mohammad Shahadat Hossain

Department of Computer Science and Engineering, University of Chittagong, University-4331, Bangladesh
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Karl Andersson

Luleå tekniska universitet; Datavetenskap
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