title: Expert knowledge integration in the data mining process with application to cardiovascular risk assessment.
abstract: The data mining process, when applied to clinical databases, suffers from critical data problems, from noisy acquisitions to missing or incomplete data points. Expert knowledge, in the form of practitioners' experience and clinical guidelines, is already used to manually correct some of these problems, while enhancing expert's confidence in such systems. In this work, we propose the Knowledge-Biased Tree (KB3), a knowledge biased decision tree inducer that is able to exploit IF THEN rules to guide the tree inducing process. The KB3 approach was tested against its unbiased counterpart, the C5.0 algorithm in the cardiovascular risk assessment task. Using a clinical dataset provided by the hospital of Sta Cruz (Lisbon, Portugal) the performance of the proposed algorithm is compared against the unbiased C5.0 and the state of the art risk score used in clinical practice (GRACE risk score).
authors.forename: M
authors.initials: M
authors.lastname: Tavares
authors.forename: S
authors.initials: S
authors.lastname: Paredes
authors.forename: T
authors.initials: T
authors.lastname: Rocha
authors.forename: P
authors.initials: P
authors.lastname: Carvalho
authors.forename: J
authors.initials: J
authors.lastname: Ramos
authors.forename: D
authors.initials: D
authors.lastname: Mendes
authors.forename: J
authors.initials: J
authors.lastname: Henriques
authors.forename: J
authors.initials: J
authors.lastname: Morais
mesh_terms: D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000453:epidemiology*; D057225:Data Mining; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D008962:Models, Theoretical; D011174:Portugal; D018570:Risk Assessment; D012307:Risk Factors