title: Modelling cardiac patient set residuals using rough sets.
abstract: Many medical studies deal with the assessment of the prognostic or diagnostic power of some particular test with respect to some particular medical condition. However, even though a test is deemed to be powerful in this respect, the test may not be strictly needed to perform for everyone. If the test is costly or invasive, this issue is of particular interest. This paper presents a methodology based on rough set theory and Boolean reasoning that can be used to identify those patients for whom performing the test is redundant or superfluous. Furthermore, the methodology enables one to automatically construct a set of descriptive and minimal if-then rules that model the patient group in need of the test. A reanalysis of a previously published real-world dataset of patients with chest pain is used as a case study.
authors.affiliation: Dept. of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway. aleks@idi.ntnu.no
authors.forename: A
authors.initials: A
authors.lastname: Ohrn
authors.forename: S
authors.initials: S
authors.lastname: Vinterbo
authors.forename: P
authors.initials: P
authors.lastname: Szymański
authors.forename: J
authors.initials: J
authors.lastname: Komorowski
mesh_terms: D003327:Coronary Disease / Q000150:complications / Q000175:diagnosis* / Q000401:mortality; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D006801:Humans; D007257:Information Theory; D009203:Myocardial Infarction / Q000209:etiology; D011336:Probability; D011379:Prognosis; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon