Self-Adaptation of Evolutionary Constructed Decision Trees by Information Spreading
Decision support systems that help physicians are becoming 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 a self-adapting evolutionary decision support model, that uses evolutionary principles for the induction of decision trees. We constructed a multi-population decision model with information spreading and inter-population competition as the self-adaptive method with the aim to improve the quality of the obtained solution. Several solutions were evolved for the classification of mitral valve prolapse syndrome. A comparison has been made with the traditional induction of decision trees. Our approach can be considered as a good choice for different kinds of real-world medical decision making, with respect to the advantages of our model and the quality of the results that we obtain, especially in various medical applications.
KeywordsGenetic Algorithm Decision Tree Evaluation Function Mitral Valve Prolapse Main Population
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