Shanbehzadeh Mostafa, Nopour Raoof, Kazemi-Arpanahi Hadi
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
J Educ Health Promot. 2022 Jan 31;11:2. doi: 10.4103/jehp.jehp_387_21. eCollection 2022.
From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19.
Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated.
After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis.
The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19.
自2019年12月起，名为COVID-19的非典型肺炎在全球呈指数级增长。它对世界卫生和经济构成了巨大威胁与挑战。医学专家在基于对COVID-19的判断做出决策时面临不确定性。因此，本研究旨在建立一种基于人工神经网络（ANN）的智能模型用于诊断COVID-19。
利用单中心登记系统，我们研究了2020年2月9日至2020年10月20日期间250例确诊COVID-19病例和150例阴性病例的记录。采用相关系数技术确定ANN模型的最显著变量。P<0.05的变量用于模型构建。我们应用反向传播技术在数据集上训练神经网络。在比较不同的神经网络配置后，获得了ANN的最佳配置，然后对其性能进行了评估。
经过特征选择过程，共确定了18个变量作为开发ANN模型的最相关预测因子。结果表明，具有9-10-15-2结构（第1层和第2层分别使用10个和15个神经元）的双嵌套循环结构，曲线下面积为0.982，灵敏度为96.4%，特异性为90.6%，准确率为94%，被作为COVID-19诊断的最佳配置模型。
所提出的基于ANN的临床决策支持系统可被视为一种适用于一线从业者的计算技术，用于早期检测、有效干预，并可能降低COVID-19患者的死亡率。
