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Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease

机译：哮喘和慢性阻塞性肺疾病的神经模糊分类

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Background This paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network. Methods Fuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo. Results Out of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained. Conclusion Our neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification. Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patient's dynamic assessment, as opposed to the solution that provides a static assessment of the patient.
机译：背景技术本文提出了一种基于模糊规则和训练有素的神经网络的哮喘和慢性阻塞性肺疾病（COPD）分类系统。方法根据全球哮喘倡议（GINA）和全球慢性阻塞性肺疾病倡议（GOLD）指南定义模糊规则和神经网络参数。对于神经网络培训，使用了从CareFusion公司的数据库中获得的一千多种医学报告。之后，萨拉热窝大学临床中心的医师对455名患者进行了系统验证。结果170例哮喘患者中，正确分类的患者为99.41％。另外，在248名COPD患者中，有99.19％的患者被正确分类。该系统在37例肺功能正常的患者中100％成功。在哮喘和COPD分类中获得了99.28％的敏感性和100％的特异性。结论我们用于哮喘和COPD分类的神经模糊系统结合了肺活量测定法和脉冲示波法系统（IOS）的测试结果，从一开始就可以实现更准确的分类。此外，使用支气管扩张和支气管激发试验，我们可以获得完整的患者动态评估，而不是提供静态患者评估的解决方案。

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