Khayamnia Monire, Yazdchi Mohammadreza, Heidari Aghile, Foroughipour Mohsen
Department of Mathematics, Payame Noor University, Tehran, Iran.
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
J Med Signals Sens. 2019 Aug 29;9(3):174-180. doi: 10.4103/jmss.JMSS_47_18. eCollection 2019 Jul-Sep.
Headache is one of the most common forms of medical complaints with numerous underlying causes and many patterns of presentation. The first step for starting the treatment is the recognition stage. In this article, the problem of primary and secondary headache diagnosis is considered, and we evaluate the use of intelligence techniques and soft computing in order to predict the diagnosis of common headaches.
A fuzzy expert-based system for the diagnosis of common headaches by Learning-From-Examples (LFE) algorithm is presented, in which Mamdani model was used in fuzzy inference engine using Max-Min as Or-And operators, and the Centroid method was used as defuzzification technique. In addition, this article has analyzed common headache using two classification techniques, and headache diagnosis based on a support vector machine (SVM) and multilayer perceptron (MLP)-based method has been proposed. The classifiers were used to recognize the four types of common headache, namely migraine, tension, headaches as a result of infection, and headaches as a result of increased intra cranial presser.
By using a dataset obtained from 190 patients, suffering from primary and secondary headaches, who were enrolled from a medical center located in Mashhad, the diagnostic fuzzy system was trained by LFE algorithm, and on an average, 123 pieces of If-Then rules were produced for fuzzy system, and it was observed that the system had the ability of correct recognition by a rate of 85%. Using the headache diagnostic system by MLP- and SVM-based decision support system, the accuracy of classification into four types improved by 88% when using the MLP and by 90% with the SVM classifier. The performance of all methods is evaluated using classification accuracy, precision, sensitivity, and specificity.
As the linguistic rules may be incomplete when human experts express their knowledge, and according to the proximity of common headache symptoms and importance of early diagnosis, the LFE training algorithm is more effective than human expert system. Favorable results obtained by the implementation and evaluation of the suggested medical decision support system based on the MLP and SVM show that intelligence techniques can be very useful for the recognition of common headaches with similar symptoms.
头痛是最常见的医学主诉之一，有众多潜在病因和多种表现形式。开始治疗的第一步是诊断阶段。在本文中，我们探讨了原发性和继发性头痛的诊断问题，并评估了智能技术和软计算在预测常见头痛诊断中的应用。
提出了一种基于模糊专家系统的常见头痛诊断方法，该系统采用示例学习（LFE）算法，在模糊推理引擎中使用Mamdani模型，以最大-最小作为或-与运算符，并采用重心法作为去模糊化技术。此外，本文还使用两种分类技术对常见头痛进行了分析，并提出了基于支持向量机（SVM）和多层感知器（MLP）的头痛诊断方法。这些分类器用于识别四种常见头痛类型，即偏头痛、紧张性头痛、感染性头痛和颅内压升高引起的头痛。
通过使用从位于马什哈德的一家医疗中心招募的190例原发性和继发性头痛患者的数据集，采用LFE算法对诊断模糊系统进行训练，平均为模糊系统生成123条If-Then规则，观察到该系统的正确识别率为85%。使用基于MLP和SVM的决策支持系统的头痛诊断系统，使用MLP时四类分类的准确率提高到88%，使用SVM分类器时提高到90%。所有方法的性能均使用分类准确率、精确率、灵敏度和特异性进行评估。
由于人类专家表达知识时语言规则可能不完整，且鉴于常见头痛症状的相似性和早期诊断的重要性，LFE训练算法比人类专家系统更有效。基于MLP和SVM的建议医疗决策支持系统的实施和评估取得了良好结果，表明智能技术在识别症状相似的常见头痛方面非常有用。
