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首页> 外文期刊>Journal of International Medical Research >Diagnosing Mitral Valve Prolapse by Improving the Predictive Power of Classifiers
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Diagnosing Mitral Valve Prolapse by Improving the Predictive Power of Classifiers

机译：通过提高分类器的预测能力诊断二尖瓣脱垂

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Mitral valve prolapse (MVP) has been described as one of the most common cardiac valvular abnormalities in industrialized countries, and can result in sudden death. This study focused on various feature selection mechanisms that might improve the predictive power of a classifier to diagnose MVP. The experiment included selection mechanisms using classical greedy feature selection approaches (forward selection and backward elimination), a genetic algorithm (GA) approach and a cellular automaton (CA) approach. The main aim of this latest approach is to use CA with GA for the data transformation phase of the knowledge discovery process. The CA—GA approach produced better results than the classical greedy approaches. The subsets of features produced by the GA and CA approaches were most appropriate for the decision tree classifier, for diagnosing MVP with the highest overall class accuracy. More importantly, the CA and GA approaches were also capable of generalizing some important knowledge concerning MVP diagnosis.
机译：在工业化国家中，二尖瓣脱垂（MVP）被描述为最常见的心脏瓣膜异常之一，并可能导致猝死。这项研究集中于各种特征选择机制，这些机制可能会提高分类器诊断MVP的预测能力。实验包括使用经典贪婪特征选择方法（前向选择和后向消除），遗传算法（GA）方法和细胞自动机（CA）方法的选择机制。这种最新方法的主要目的是将CA与GA一起用于知识发现过程的数据转换阶段。与经典的贪婪方法相比，CA-GA方法产生了更好的结果。 GA和CA方法产生的特征子集最适合决策树分类器，以最高的总体分类精度诊断MVP。更重要的是，CA和GA方法还能够概括一些有关MVP诊断的重要知识。

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