Cost-sensitive learning in medicine

• Type: Chapter or Part of a Book
• Date: 2009

Authors

Freitas, A (Author) Other; Pavel Brazdil (Author) FEP; Costa-Pereira A (Author) FMUP

Book

Title: Data Mining and Medical Knowledge Management: Cases and Applications

Pages: 57-75

ISBN: 9781605662183

Electronic ISBN: 9781605662190

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-009-GYG

DOI: 10.4018/978-1-60566-218-3.ch003

Abstract (EN): 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. © 2009, IGI Global.

Language: English

Type (Professor's evaluation): Scientific