Povalej P, Lenic M, Zorman M, Kokol P, Dinevski D
Laboratory for system design, Faculty for electrical engineering and computer science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Comput Methods Programs Biomed. 2005 Dec;80 Suppl 1:S95-S105. doi: 10.1016/s0169-2607(05)80010-0.
Intelligent medical systems are a special kind of medical software in general, and just as any medical software system they should make accurate presumptions. However, accuracy of intelligent medical systems is highly dependent on various factors such as: choosing an appropriate basic method (i.e. decision trees, neural networks), induction method (i.e. purity measures) and appropriate support methods (i.e. discretization, pruning, boosting). In this paper we present the results of extensive research of the above alternatives on 54 UCI databases and their influence on the accuracy of decision trees, which constitute one of the most desirable forms of intelligent medical systems. We also introduce new hybrid purity measures that on some databases outperform other purity measures. The results presented here show that the selection of the right purity measure with the proper discretization method and application of the boosting method can really make a difference in terms of higher accuracy of induced decision trees. Thereafter choosing the appropriate factors that can increase the accuracy of the induced decision tree is a very demanding and time-consuming task.
智能医疗系统总体上是一种特殊的医疗软件，与任何医疗软件系统一样，它们应该做出准确的推测。然而，智能医疗系统的准确性高度依赖于各种因素，例如：选择合适的基本方法（即决策树、神经网络）、归纳方法（即纯度度量）和合适的支持方法（即离散化、剪枝、增强）。在本文中，我们展示了对上述备选方案在54个UCI数据库上进行广泛研究的结果，以及它们对决策树准确性的影响，决策树是智能医疗系统最理想的形式之一。我们还引入了新的混合纯度度量，在某些数据库上，这些度量优于其他纯度度量。此处展示的结果表明，选择正确的纯度度量、适当的离散化方法并应用增强方法，对于提高归纳决策树的准确性而言确实会产生显著影响。此后，选择能够提高归纳决策树准确性的合适因素是一项非常艰巨且耗时的任务。
