title: Extraction of rules for tuberculosis diagnosis using an artificial neural network.
abstract: The treatment of tuberculosis (TB) is a major challenge throughout the world. The Western Cape Region of South Africa has the highest occurrence of TB in the world. Here, TB is increasing due to improperly managed treatment programmes and inadequate facilities. The development of rules to aid medical practitioners in the early and accurate diagnosis of tuberculosis should prove worthwhile. A method to extract such diagnostic rules from an artificial neural network is presented. These rules accurately represent the knowledge embedded in the "raw" TB data.
authors.affiliation: Department of Informatics, University of Pretoria, South Africa. hlviktor@econ.up.ac.za
authors.forename: H L
authors.initials: HL
authors.lastname: Viktor
authors.forename: I
authors.initials: I
authors.lastname: Cloete
authors.forename: N
authors.initials: N
authors.lastname: Beyers
mesh_terms: D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer; D014376:Tuberculosis / Q000175:diagnosis*