title: Rule induction and instance-based learning applied in medical diagnosis.
abstract: Machine learning methods have been applied in a variety of medical domains in order to improve medical decision making. Improved medical diagnosis and prognosis can be achieved through automatic analysis of patient data stored in medical records, i.e., by learning from past experience. Given patient records with corresponding diagnoses, machine learning methods are able to classify new cases either through constructing explicit rules that generalize the training cases (e.g., rule induction) or by storing (some of) the training cases for reference (instance-based learning). This paper presents the methodologies of rule induction and instance-based learning and their application to medical diagnosis, in particular, the problem of early diagnosis of rheumatic diseases. It also discusses the possibility to use existing expert knowledge to support the learning process and the utility of such knowledge.
authors.affiliation: Jozef Stefan Institute, Ljubljana, Slovenia. Saso.Dzeroski@ijs.si
authors.forename: S
authors.initials: S
authors.lastname: Dzeroski
authors.forename: N
authors.initials: N
authors.lastname: Lavrac
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D015203:Reproducibility of Results; D012216:Rheumatic Diseases / Q000145:classification* / Q000175:diagnosis*; D012986:Software Validation