Developing an Intelligent System for Diagnosis of COVID-19 Based on Artificial Neural Network

Abstract

An outbreak of atypical pneumonia termed coronavirus disease 2019 (COVID-19) has spread worldwide since the beginning of 2020. It poses a significant threat to the global health and the economy. Physicians face ambiguity in their decision-making for COVID-19 diagnosis and treatment. In this respect, designing an intelligent system for early diagnosis of the disease is critical for mitigating virus spread and resource optimization. This study aimed to establish an artificial neural network (ANNs)-based clinical model to diagnose COVID-19. The retrospective dataset used in this study consisted of 400 COVID-19 case records (250 positives vs. 150 negatives) and 18 columns for the diagnostic features. The backpropagation technique was used to train a neural network. After designing multiple neural network configurations, the area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, and specificity values were calculated to measure the model performance. The two nested loops architecture of 9-10-15-2 (10 and 15 neurons used in layer one and layer two, respectively) with the ROC of 98.2%, sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94 % were introduced as the best configuration model for COVID-19 diagnosis. ANN is valuable as a decision-support tool for clinicians to improve the COVID-19 diagnosis. It is promising to implement the ANN model to improve the accuracy and speed of the COVID-19 diagnosis for timely screening, treatment, and careful monitoring. Further studies are warranted for verifying and improving the current ANN model.

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

“…These studies also recommended the use of CDSS in future research. Finally, 68 articles met all the inclusion criteria 5,17,34–99 . The flowchart of the selection process is shown in Figure 1.…”

“…Types of CDSS to assist in diagnosing COVID‐19 are shown in Figure 4. Most of the studies used ICDSS based on ML (nonknowledge‐based CDSS) ( n = 52 [76.5%]) 34–85 . In these studies, the most common methods for designing CDSS were CNN ( n = 33), 38,40–42,45–47,49–52,54,56–69,71,72,78,82–85 SVM ( n = 8), 35,36,39,43,44,54,56,57 RF ( n = 7), 34,35,37,39,42,44,55 and KNN ( n = 7) 36,37,39,42,43,55,56 (Table 1 and Appendix ).…”

“…Most studies used a combination of performance metrics to evaluate CDSS ( n = 40) (Appendix ). In most of these studies, accuracy ( n = 43), 35–40,42–47,49–69,71,72,76–78,80–83,85 sensitivity (recall) ( n = 35) 35–37,39,41–44,46,47,49–54,56,57,61,63–69,71,72,76,78,81–85 and F1‐score ( n = 26) 34–36,39,41–43,46,47,50,52–54,56,57,60,61,63,64,66,68,69,81,82,84,85 were used (Table 2). Other metrics for evaluating ICDSS based on ML were the Matthews correlation coefficient (MCC), 52,56,58 receiver operating characteristic (ROC) curve, 34,36,70 and so on (Appendix ).…”

“…The most frequently used method for this purpose was the ICDSS based on ML (nonknowledge‐based CDSS), followed by ICDSS based on ES (knowledge‐based CDSS). Most studies have indicated that using CDSS have positively impacted the accurate diagnosis of COVID‐19 5,17,34–62,64–84,86–90,92–99 …”

Authentication of Covid-19 Vaccines Using Synchronous Fluorescence Spectroscopy

“…When combined with machine learning algorithms (MLAs), spectroscopic data of medicines have informed about identity, authenticity, manufacturing sources and/or geographical location [ 28 ]. Part of MLAs, artificial neural network (ANN) algorithms have shown accuracy in detecting Covid-19 disease diagnosis and prediction of mortality [ 29 – 31 ] Subsequently, this research utilises SF and MLAs (including ANN) for authentication of DNA- and RNA-based vaccines obtained from different manufacturers. The work explores three MLAs for classification of the measured vaccines and understanding patterns between vaccines of the same and different manufactures.…”