title: The art of building decision trees.
abstract: Decision support systems that help physicians are becoming a very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable, and quick response. One of the most viable among models are decision trees, already successfully used for many medical decision-making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision support models using four different approaches. We took statistical analysis, a MtDeciT, in our laboratory developed tool for building decision trees with a classical method, the well-known C5.0 tool and a self-adapting evolutionary decision support model that uses evolutionary principles for the induction of decision trees. Several solutions were evolved for the classification of metabolic and respiratory acidosis (MRA). A comparison between developed models and obtained results has shown that our approach can be considered as a good choice for different kinds of real-world medical decision making.
authors.affiliation: Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia. ¿Spela.Hleb, Kokol¿@uni-mb.si
authors.forename: S H
authors.initials: SH
authors.lastname: Babic
authors.forename: P
authors.initials: P
authors.lastname: Kokol
authors.forename: V
authors.initials: V
authors.lastname: Podgorelec
authors.forename: M
authors.initials: M
authors.lastname: Zorman
authors.forename: M
authors.initials: M
authors.lastname: Sprogar
authors.forename: M M
authors.initials: MM
authors.lastname: Stiglic
mesh_terms: D000142:Acidosis, Respiratory / Q000097:blood / Q000175:diagnosis / Q000209:etiology; D000465:Algorithms; D002648:Child; D020000:Decision Support Systems, Clinical / Q000458:organization & administration; D003663:Decision Trees; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity