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Learning Cost-Sensitive Decision Trees to Support Medical Diagnosis

Learning Cost-Sensitive Decision Trees to Support Medical Diagnosis

Alberto Freitas, Altamiro Costa-Pereira
Copyright: © 2010 |Pages: 21
ISBN13: 9781605667485|ISBN10: 160566748X|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch013
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MLA

Freitas, Alberto, and Altamiro Costa-Pereira. "Learning Cost-Sensitive Decision Trees to Support Medical Diagnosis." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 287-307. https://doi.org/10.4018/978-1-60566-748-5.ch013

APA

Freitas, A. & Costa-Pereira, A. (2010). Learning Cost-Sensitive Decision Trees to Support Medical Diagnosis. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 287-307). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch013

Chicago

Freitas, Alberto, and Altamiro Costa-Pereira. "Learning Cost-Sensitive Decision Trees to Support Medical Diagnosis." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 287-307. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch013

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Abstract

Classification plays an important role in medicine, especially for medical diagnosis. Real-world medical applications often require classifiers that minimize the total cost, including costs for wrong diagnosis (misclassifications costs) and diagnostic test costs (attribute costs). There are indeed many reasons for considering costs in medicine, as diagnostic tests are not free and health budgets are limited. In this chapter, the authors have defined strategies for cost-sensitive learning. They have developed an algorithm for decision tree induction that considers various types of costs, including test costs, delayed costs and costs associated with risk. Then they have applied their strategy to train and to evaluate cost-sensitive decision trees in medical data. Generated trees can be tested following some strategies, including group costs, common costs, and individual costs. Using the factor of “risk” it is possible to penalize invasive or delayed tests and obtain patient-friendly decision trees.

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