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Cost-Sensitive Learning in Medicine

Cost-Sensitive Learning in Medicine

Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
Copyright: © 2009 |Pages: 19
ISBN13: 9781605662183|ISBN10: 1605662186|ISBN13 Softcover: 9781616926007|EISBN13: 9781605662190
DOI: 10.4018/978-1-60566-218-3.ch003
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MLA

Freitas, Alberto, et al. "Cost-Sensitive Learning in Medicine." Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, et al., IGI Global, 2009, pp. 57-75. https://doi.org/10.4018/978-1-60566-218-3.ch003

APA

Freitas, A., Brazdil, P., & Costa-Pereira, A. (2009). Cost-Sensitive Learning in Medicine. In P. Berka, J. Rauch, & D. Zighed (Eds.), Data Mining and Medical Knowledge Management: Cases and Applications (pp. 57-75). IGI Global. https://doi.org/10.4018/978-1-60566-218-3.ch003

Chicago

Freitas, Alberto, Pavel Brazdil, and Altamiro Costa-Pereira. "Cost-Sensitive Learning in Medicine." In Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, Jan Rauch, and Djamel Abdelkader Zighed, 57-75. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-218-3.ch003

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Abstract

This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). In fact, as in other professional areas, both diagnostic tests and its associated misclassification errors can have significant financial or human costs, including the use of unnecessary resource and patient safety issues. This chapter presents some concepts related to cost-sensitive learning and cost-sensitive classification and its application to medicine. Different types of costs are also present, with an emphasis on diagnostic tests and misclassification costs. In addition, an overview of research in the area of cost-sensitive learning is given, including current methodological approaches. Finally, current methods for the cost-sensitive evaluation of classifiers are discussed.

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