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  1. University of Computer Studies, Yangon
  2. Faculty of Computer Science

Ensemble Learning Method for Enhancing Healthcare Classification

http://hdl.handle.net/20.500.12678/0000004581
http://hdl.handle.net/20.500.12678/0000004581
7d985d11-a321-422d-8118-7fff6fb24156
2f206bbb-5b51-49f1-8cb9-47734ab0577e
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Title
Title Ensemble Learning Method for Enhancing Healthcare Classification
Language en
Publication date 2020-02-28
Authors
Mung, Pau Suan
Phyu, Sabai
Description
Ensemble learning technique is proposed in this paper for better efficiency of healthcareclassification and prediction. Healthcare industry is an ever-increasing rise in the number of doctors, patients,medicines and medical records. Medical history records are beneficial for not only individual but also humansociety. Three popular machine learning algorithms, namely Naïve Bayes, Support Vector Machine andDecision Tree are applied on this history data as base learners. Two forms of ensemble learning namelybagging and boosting are applied with each base learner for better accuracy than using individually.Comparison results are presented and the experiments show that ensemble classifiers perform better than thebase classifier alone. Cervical cancer dataset is used as case study.
Keywords
Ensemble learning, Base learners, Machine learning, Bagging and boosting
Identifier 978-981-14-4787-7
Journal articles
Proceedings of the 10th International Workshop on Computer Science and Engineering (WCSE 2020)
Conference papers
Books/reports/chapters
Thesis/dissertations
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