| Reliable confidence measures for medical diagnosis with evolutionary algorithms. | |
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MedLine Citation:
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PMID: 21062682 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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Conformal Predictors (CPs) are machine learning algorithms that can provide predictions complemented with valid confidence measures. In medical diagnosis, such measures are highly desirable, as medical experts can gain additional information for each machine diagnosis. A risk assessment in each prediction can play an important role for medical decision making, in which the outcome can be critical for the patients. Several classical machine learning methods can be incorporated into the CP framework. In this paper, we propose a CP that makes use of evolved rule sets generated by a genetic algorithm (GA). The rule-based GA has the advantage of being human readable. We apply our method on two real-world datasets for medical diagnosis, one dataset for breast cancer diagnosis, which contains data gathered from fine needle aspirate of breast mass; and one dataset for ovarian cancer diagnosis, which contains proteomic patterns identified in serum. Our results on both datasets show that the proposed method is as accurate as the classical techniques, while it provides reliable and useful confidence measures. |
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Authors:
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Antonis Lambrou; Harris Papadopoulos; Alex Gammerman |
Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2010-11-09 |
Journal Detail:
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Title: IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society Volume: 15 ISSN: 1558-0032 ISO Abbreviation: IEEE Trans Inf Technol Biomed Publication Date: 2011 Jan |
Date Detail:
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Created Date: 2011-01-10 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9712259 Medline TA: IEEE Trans Inf Technol Biomed Country: United States |
Other Details:
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Languages: eng Pagination: 93-9 Citation Subset: IM |
Affiliation:
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Computer Learning Research Centre and the Department of Computer Science, Royal Holloway, University of London, Surrey, UK. A.Lambrou@cs.rhul.ac.uk |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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