Tsipouras Markos G, Voglis Costas, Fotiadis Dimitrios I
Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece.
IEEE Trans Biomed Eng. 2007 Nov;54(11):2089-105. doi: 10.1109/TBME.2007.893500.
A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.
本文提出了一种用于模糊专家系统自动化开发的方法。其思路是从由清晰规则描述的清晰模型开始，然后将其转换为一组模糊规则，从而创建一个模糊模型。模型参数的调整通过随机全局优化过程来执行。通过将所提出的方法应用于与心血管疾病相关的问题来进行测试，例如自动心律失常搏动分类和自动缺血性搏动分类，这些问题除了是众所周知的基准外，因其明显的医学诊断重要性而特别受关注。对于这两个问题，初始规则集由心脏病专家确定，并使用麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库和欧洲ST - T数据库来优化模糊模型的参数并评估模糊专家系统。在这两种情况下，结果都表明从简单的初始清晰模型到更复杂的模糊模型，性能有所提升，证明了所提出框架的科学附加值。此外，与“黑箱”方法（如神经网络和其他技术）相比，能够解释所创建的模糊专家系统的决策是一个主要优势。
