Learning cost-sensitive decision trees to support medical diagnosis

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

Authors

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

Book

Title: Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications

Pages: 287-307

ISBN: 9781605667485

Electronic ISBN: 9781605667492

Indexing

Scopus - 7 Citations

Other information

Authenticus ID: P-009-BJ9

DOI: 10.4018/978-1-60566-748-5.ch013

Abstract (EN): 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. © 2010, IGI Global.

Language: English

Type (Professor's evaluation): Scientific