Cost-Sensitive Learning in Medicine

Cost-Sensitive Learning in Medicine

Alberto Freitas (University of Porto and CINTESIS, Portugal), Pavel Brazdil (University of Porto and LIAAD - INESC Porto L.A., Portugal) and Altamiro Costa-Pereira (University of Porto, Portugal and CINTESIS, Portugal)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/978-1-60960-818-7.ch607
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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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Cost-Sensitive Classification

Classification is one of the main tasks in knowledge discovery and data mining (Mitchell, 1997). It has been object of study in areas as machine learning, statistics and neural networks. There are many approaches for classification, including decision trees, Bayesian classifiers, neural classifiers, discriminant analysis, support vector machines, and rule induction, among many others.

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