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首页> 外文期刊>Expert Systems >An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism
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An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism

机译：一种改进的医学数据分类方法：具有模糊资源分配机制的人工免疫识别系统

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摘要

The artificial immune recognition system (AIRS) has been shown to be an efficient approach' to tackling a variety of problems such as machine learning benchmark problems and medical classification problems. In this study, the resource allocation mechanism of AIRS was replaced with a new one based on fuzzy logic. The new system, named Fuzzy-AIRS, was used as a classifier in the classification of three well-known medical data sets, the Wisconsin breast cancer data set ( WBCD), the Pima Indians diabetes data set and the ECG arrhythmia data set. The performance of the Fuzzy-AIRS algorithm was tested for classification, accuracy, sensitivity and specificity values, confusion matrix, computation time and receiver operating characteristic curves. Also, the AIRS and Fuzzy-AIRS algorithms were compared with respect to the amount of resources required in the execution of the algorithm. The highest classification accuracy obtained from applying the AIRS and Fuzzy-AIRS algorithms using 10-fold cross-validation was, respectively, 98.53% and 99.00% for classification of WBCD; 79.22% and 84.42% for classification of the Pima Indians diabetes data set; and 100%> and 92.86% for classification of the ECG arrhythmia data set. Hence, these results show that Fuzzy-A IRS can be used as an effective classifier for medical problems.
机译：事实证明，人工免疫识别系统（AIRS）是解决各种问题（例如机器学习基准问题和医学分类问题）的有效方法。在这项研究中，AIRS的资源分配机制被一种基于模糊逻辑的新机制所取代。名为Fuzzy-AIRS的新系统在三个著名医学数据集（威斯康星州乳腺癌数据（WBCD），皮马印第安人糖尿病数据和ECG心律失常数据集）的分类中用作分类器。针对分类，准确性，灵敏度和特异性值，混淆矩阵，计算时间和接收器工作特性曲线，测试了Fuzzy-AIRS算法的性能。此外，针对执行该算法所需的资源量，对AIRS和Fuzzy-AIRS算法进行了比较。通过使用AIRS和Fuzzy-AIRS算法进行10倍交叉验证获得的最高分类精度分别为WBCD的98.53％和99.00％。对比马印第安人糖尿病数据集进行分类的比例分别为79.22％和84.42％；心电图心律失常数据集的分类分别为100％和92.86％。因此，这些结果表明，Fuzzy-A IRS可以用作医学问题的有效分类器。

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