Engineering for intelligent systems

Abstract

Intelligent systems that help physicians are becoming an 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 classical method, the well-known C5.0 tool and a self-adapting intelligent system (IS). Since conceptual simple decision making models with the possibility of automatic learning should be considered for performing such tasks, decision trees are a very suitable candidate. There are many various methods for decision tree construction proposed during evolutionary decision support model, that use 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.