期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 119, 期 2, 页码 63-76出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2015.03.003
关键词
Artificial immune recognition system; Brain metastasis; Imbalance dataset; Lung cancer; Borderline-synthetic minority over sampling technique
类别
资金
- National Science Council Taiwan
Classifying imbalanced data in medical informatics is challenging. Motivated by this issue, this study develops a classifier approach denoted as BSMAIRS. This approach combines borderline synthetic minority oversampling technique (BSM) and artificial immune recognition system (AIRS) as global optimization searcher with the nearest neighbor algorithm used as a local classifier. Eight electronic medical datasets collected from University of California, Irvine (UCI) machine learning repository were used to evaluate the effectiveness and to justify the performance of the proposed BSMAIRS. Comparisons with several well-known classifiers were conducted based on accuracy, sensitivity, specificity, and G-mean. Statistical results concluded that BSMAIRS can be used as an efficient method to handle imbalanced class problems. To further confirm its performance, BSMAIRS was applied to real imbalanced medical data of lung cancer metastasis to the brain that were collected from National Health Insurance Research Database, Taiwan. This application can function as a supplementary tool for doctors in the early diagnosis of brain metastasis from lung cancer. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据