Çelik Ufuk, Yurtay Nilüfer, Koç Emine Rabia, Tepe Nermin, Güllüoğlu Halil, Ertaş Mustafa
Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, 54187 Sakarya, Turkey.
Department of Neurology, Faculty of Medicine, Balikesir University, 10145 Balikesir, Turkey.
Comput Math Methods Med. 2015;2015:465192. doi: 10.1155/2015/465192. Epub 2015 May 4.
The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.
本研究评估了免疫系统算法的诊断准确性，目的是对与任何器质性病因无关的原发性头痛类型进行分类。它们分为四种类型：偏头痛、紧张性头痛、丛集性头痛和其他原发性头痛。在考虑到这一主要目标后，要求三位不同的神经科医生将850名患者的病历录入我们项目网站上基于网络的专家系统。在评估过程中，人工免疫系统（AIS）被用作分类算法。AIS是受生物免疫系统机制启发的分类算法，该机制具有显著且独特的能力。这些算法模拟免疫系统的特性，如识别、学习和记忆过程，以便用于分类、优化或模式识别。根据结果，本研究中使用的分类器的准确率达到了95%至99%的成功区间，只有一个不方便的分类器准确率为71%。
