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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A fuzzy classification model for myocardial infarction risk assessment
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A fuzzy classification model for myocardial infarction risk assessment

机译：心肌梗死风险评估模糊分类模型

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摘要

The use of data mining approaches for analyzing patients trace in different medical databases has become an important research field especially with the evolution of these methods and their contributions in medical decision support. In this paper, we develop a new clinical decision support system (CDSS) to diagnose Coronary Artery Diseases (CAD). According to CAD experts, Angiography is most accurate CAD diagnosis technique. However, it has many aftereffects and is very costly. Existing studies showed that CAD diagnosis requires heterogeneous patients traces from medical history while applying data mining techniques to achieve high accuracy. In this paper, an automatic approach to design CDSS for CAD assessment is proposed. The proposed diagnosis model is based on Random Forest algorithm, C5.0 decision tree algorithm and Fuzzy modeling. It consists of two stages: first, Random Forest algorithm is used to rank the features and a C5.0 decision tree based approach for crisp rule generation is developed. Then, we created the fuzzy inference system. The generation of fuzzy weighted rules is carried out automatically from the previous crisp rules. Moreover, a critical issue about the CDSS is that some values of the features are missing in most cases. A new method to deal with the problem of missing data, which allows evaluating the similarity despite the missing information, was proposed. Finally, experimental results underscore very promising classification accuracy of 90.50% while optimizing training time using UCI (the University of California at Irvine) heart diseases datasets compared to the previously reported results.
机译：使用数据采矿方法来分析不同医疗数据库中的患者痕迹已成为一个重要的研究领域，特别是随着这些方法的演变及其在医学决策支持中的贡献。在本文中，我们开发了一种新的临床决策支持系统（CDSS）来诊断冠状动脉疾病（CAD）。根据CAD专家的说法，血管造影是最精确的CAD诊断技术。然而，它有许多后遗症并且非常昂贵。现有的研究表明，CAD诊断需要异质患者，在应用数据挖掘技术的同时，在应用数据挖掘技术中实现高精度。本文提出了一种对CAD评估设计CDS的自动方法。所提出的诊断模型基于随机林算法，C5.0决策树算法和模糊建模。它由两个阶段组成：首先，开发了随机森林算法对特征进行排名，并开发了基于C5.0决策树的CRIP规则生成方法。然后，我们创建了模糊推理系统。模糊加权规则的生成自动从以前的清晰规则进行。此外，关于CDS的关键问题是在大多数情况下缺少一些功能的值。提出了一种解决缺失数据问题的新方法，其允许尽管缺少信息来评估相似之处。最后，实验结果强调了90.50％的非常有前途的分类准确度，同时优化使用UCI（欧文大学）心脏病数据集的培训时间与先前报道的结果相比。

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