Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Abstract

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

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 …”

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

“…There is consistent evidence that an increased BMI is associated with higher health costs, and these costs are expected to increase as obesity. Modelling uses machine-learning methods, in which the machine learns from the data and uses it to forecast new data [ 1 , 2 ]. The most commonly predictive analytic model used is regression [ 3 – 6 ].…”