Hosseini Seyed Ahmad, Jamshidnezhad Amir, Zilaee Marzie, Fouladi Dehaghi Behzad, Mohammadi Abbas, Hosseini Seyed Mohsen
Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Department of Nutrition, Faculty of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
JMIR Med Inform. 2020 Jul 6;8(7):e17580. doi: 10.2196/17580.
Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma.
The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma.
A genetic algorithm-modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy.
The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm-modified neural network predicted the level of effect with high accuracy for anti-heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV), forced vital capacity (FVC), the ratio of FEV/FVC, and forced expiratory flow (FEF) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV: 98.1%; FVC: 97.5%; FEV/FVC ratio: 97%; and FEF: 96.7%, respectively).
The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.
哮喘通常与慢性气道炎症相关，是每年超过百万例死亡的根本原因。藏红花，通常被称为番红花，以传统药物形式使用时，已显示出抗炎作用，这可能对哮喘患者有益。
本研究的目的是开发一种使用人工神经网络的临床预测系统，以检测藏红花补充剂对过敏性哮喘患者的影响。
开发了一种遗传算法改进的神经网络预测系统，以利用从哮喘患者的临床、免疫、血液学和人口统计学信息中提取的特征来检测藏红花的有效性水平。该研究纳入了年龄在18至65岁之间的轻度或中度过敏性哮喘男性（n = 40）和女性（n = 40）的数据。该模型的目的是估计和预测藏红花补充剂对每个哮喘风险因素的影响水平，并预测哮喘患者的缓解程度。使用遗传算法为临床预测系统提取输入特征，以提高其预测性能。此外，还为使用藏红花补充剂疗法对哮喘患者进行分类的人工神经网络组件开发了一个优化模型。
临床预测系统的最佳总体性能是训练和测试数据的准确率大于99%。遗传算法改进的神经网络对测试数据的抗热休克蛋白（anti-HSP）、高敏C反应蛋白（hs-CRP）、第一秒用力呼气量（FEV）、用力肺活量（FVC）、FEV/FVC比值和用力呼气流量（FEF）的影响水平预测准确率很高（anti-HSP：96.5%；hs-CRP：98.9%；FEV：98.1%；FVC：97.5%；FEV/FVC比值：97%；FEF：96.7%）。
本研究开发的临床预测系统在预测藏红花补充剂对过敏性哮喘患者的影响方面是有效的。这种临床预测系统可能有助于临床医生尽早确定哮喘的哪些临床因素在治疗过程中会得到改善，并在此过程中帮助临床医生为哮喘患者制定有效的治疗方案。
