Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules
Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules
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DOI：
10.1016/j.jksuci.2011.09.002
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发表时间：
2012-01-01
影响因子：
6.9
通讯作者：
Anooj, P. K.
中科院分区：
文献类型：
--
作者：
Anooj, P. K.
近年来，随着人们对自身健康的关注，医疗领域应用的开发已成为最活跃的研究领域之一。医学领域应用的一个例子是基于计算机辅助诊断方法的心脏病检测系统，其中数据是从其他一些来源获得的，并基于基于计算机的应用程序进行评估。早期，计算机的使用是建立一个基于知识的临床决策支持系统，该系统使用医学专家的知识并将这些知识手动转换为计算机算法。这个过程非常耗时，并且实际上取决于医学专家的意见，而这些意见可能是主观的。为了解决这个问题，机器学习技术已经被开发出来，可以从示例或原始数据中自动获取知识。在这里，提出了一种用于心脏病诊断的加权模糊基于规则的临床决策支持系统（CDSS），自动从患者的临床数据中获取知识。所提出的用于心脏病患者风险预测的临床决策支持系统包括两个阶段：（1）用于生成加权模糊规则的自动化方法；（2）开发基于模糊规则的决策支持系统。在第一阶段，我们使用挖掘技术、属性选择和属性权重方法来获得加权模糊规则。然后，根据加权模糊规则和选择的属性构建模糊系统。最后，使用从 UCI 存储库获得的数据集对所提出的系统进行实验，并利用准确性、灵敏度和特异性将系统的性能与基于神经网络的系统进行比较。 (C) 2011 年沙特国王大学。由 Elsevier B.V. 制作和托管。保留所有权利。
As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer-based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts' opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient's clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity. (C) 2011 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.