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Improved concept drift handling in surgery prediction and other applications
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2015 (English)In: Knowledge and Information Systems, ISSN 0219-1377, Vol. 44, no 1, 177-196 p.Article in journal (Refereed) Published
Abstract [en]

The article presents a new algorithm for handling concept drift: the Trigger-based Ensemble (TBE) is designed to handle concept drift in surgery prediction but it is shown to perform well for other classification problems as well. At the primary care, queries about the need for surgical treatment are referred to a surgeon specialist. At the secondary care, referrals are reviewed by a team of specialists. The possible outcomes of this review are that the referral: (i) is canceled, (ii) needs to be complemented, or (iii) is predicted to lead to surgery. In the third case, the referred patient is scheduled for an appointment with a surgeon specialist. This article focuses on the binary prediction of case three (surgery prediction). The guidelines for the referral and the review of the referral are changed due to, e.g., scientific developments and clinical practices. Existing decision support is based on the expert systems approach, which usually requires manual updates when changes in clinical practice occur. In order to automatically revise decision rules, the occurrence of concept drift (CD) must be detected and handled. The existing CD handling techniques are often specialized; it is challenging to develop a more generic technique that performs well regardless of CD type. Experiments are conducted to measure the impact of CD on prediction performance and to reduce CD impact. The experiments evaluate and compare TBE to three existing CD handling methods (AWE, Active Classifier, and Learn++) on one real-world dataset and one artificial dataset. TBA significantly outperforms the other algorithms on both datasets but is less accurate on noisy synthetic variations of the real-world dataset.

Place, publisher, year, edition, pages
Springer , 2015. Vol. 44, no 1, 177-196 p.
Keyword [en]
online learning, incremental learning, machine learning, concept drift, BigData@BTH
National Category
Computer Science Medical Laboratory and Measurements Technologies
URN: urn:nbn:se:bth-6694DOI: 10.1007/s10115-014-0756-9ISI: 000356297500008Local ID: diva2:834225

Available from: 2014-06-05 Created: 2014-06-04 Last updated: 2015-09-14Bibliographically approved

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