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In the case of communicable diseases, such as COVID-19, effective and quick testing techniques make it easier to identify a contaminated person, so that he or she can be easily isolated. To predict COVID-19-infected individuals through chest computed tomography scans, this study suggests an effective feature selection technique incorporated in clinical decision support system that may be used for testing. After pre-processing, we retrieved 213 features from the chest computed tomography images of a public data set with 2482 images. Then, in a two-step process, the most significant features for recognizing the difference between COVID patients and healthy individuals are selected. Initially, the Chi-square test selects 75% of the initial extracted features, which are then forwarded to three nature-inspired computing algorithms: the cuckoo search optimization algorithm, a teaching–learning-based optimization algorithm, and a hybrid of these two for further optimization. The finally selected reduced feature set and five machine learning classifiers are then employed to classify these computed tomography images. Twenty-four experiments using fivefold and tenfold cross-validation have been performed to find the best values for eight statistical efficiency evaluation metrics. Our suggested approach achieves a notable accuracy of 95.99%, the best mean intersection over union of 0.9655, and the highest area under curve of 0.9966. XGBoost delivers more effective, promising, and comparable results when compared to other ML classifiers. Our suggested testing approach will benefit frontline workers and the state by providing routine and cost-effective testing, and faster results. [ABSTRACT FROM AUTHOR]

The emergence of COVID-19 in late 2019 in Wuhan, China, has led to a global health crisis that has claimed many lives worldwide. A thorough understanding of the available COVID-19 datasets can enable healthcare professionals to identify cases at an early stage. This study presents an innovative pipeline-based framework for predicting survival and mortality in patients with COVID-19 by leveraging the Mexican COVID-19 patient dataset (COVID-19-MPD dataset). Preprocessing plays a pivotal role in ensuring that the framework delivers high-quality outcomes. We deploy various machine learning models with optimized hyperparameters within the framework. Through consistent experimental conditions and dataset utilization, we conducted multiple experiments employing diverse preprocessing techniques and models to maximize the area under the receiver operating characteristic curve (AUC) for COVID-19 prediction. Given the considerable dimensions of the dataset, feature selection is crucial for identifying factors influencing COVID-19 mortality or survival. We employ feature dimension reduction methods, such as principal component analysis and independent component analysis, in addition to feature selection techniques such as maximum relevance minimum redundancy and permutation feature importance. Impactful features related to patient outcomes can significantly aid experts in disease management by enhancing treatment efficacy and control measures. Following various experiments with standardized data and AUC assessment using the k-nearest neighbor algorithm with four components, the proposed framework achieves optimal results, attaining an AUC of 100%. Given its effectiveness in COVID-19 prediction, this framework has the potential for integration into medical decision support systems. [ABSTRACT FROM AUTHOR]

Problem statement: The global COVID-19 pandemic has had devastating consequences, emphasizing the need for swift and reliable coronavirus patient detection to enhance treatment and reduce transmission. Methodology: The extracted features are fed into a hybrid deep classifier that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) layers. The recurrent dropout technique is employed within the BiLSTM layer as a regularization method to prevent overfitting. This architecture leverages the strength of CNNs in feature extraction and the sequential memory handling of BiLSTMs. The research introduces a specialized optimization algorithm, the Penguin Pelican Optimizer (PPO), which is a fusion of characteristics from the Emperor penguin optimizer and the Pelican Optimization algorithm. This algorithm is designed to fine-tune and enhance the efficiency of the system by optimizing model parameters, weights, and biases. Results: The findings of this study are highly encouraging. The proposed Penguin Pelican Optimization-based recurrent dropout-enabled hybrid deep CNN-BILSTM approach achieved remarkable results, with accuracy rates of 95.57%, 96.08%, and 95.54% for training percentage, and 95.75%, 96.28%, and 95.70% for k-fold. These outcomes surpass the performance of existing methods. This research underscores the practicality and efficiency of the developed methodology for COVID-19 prediction. It offers valuable insights that can be instrumental in ensuring timely and accurate patient management, thus contributing significantly to the global effort to combat the pandemic. Conclusion: This research underscores the effectiveness and efficiency of the developed approach for COVID-19 prediction, providing valuable insights for improved patient management in the battle against the pandemic. [ABSTRACT FROM AUTHOR]

The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus. [ABSTRACT FROM AUTHOR]

The rapid spread of COVID-19, caused by the SARS-CoV-2 virus, has had and continues to pose a significant threat to global health. We propose a predictive model based on the gated recurrent unit (GRU) that investigates the influence of non-pharmaceutical interventions (NPIs) on the progression of COVID-19. The proposed model is validated by case studies for multiple states in the United States. It should be noted that the proposed model can be generalized to other regions of interest. The results show that the predictive model can achieve accurate forecasts across the US. The forecast is then utilized to identify the optimal mitigation policies. The goal is to identify the best stringency level for each policy that can minimize the total number of new COVID-19 cases while minimizing the mitigation costs. A meta-heuristics method, named multi-population evolutionary algorithm with differential evolution (MPEA-DE), has been developed to identify optimal mitigation strategies that minimize COVID-19 infection cases while reducing economic and other negative implications. We compared the optimal mitigation strategies identified by the MPEA-DE model with three baseline search strategies. The results show that MPEA-DE performs better than other baseline models based on prescription dominance. [ABSTRACT FROM AUTHOR]

Nowadays, humanity is facing one of the most dangerous pandemics known as COVID-19. Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the world. Positive patients are often suffering from different symptoms that can vary from mild to severe including cough, fever, sore throat, and body aches. In more dire cases, infected patients can experience severe symptoms that can cause breathing difficulties which lead to stern organ failure and die. The medical corps all over the world are overloaded because of the exponentially myriad number of contagions. Therefore, screening for the disease becomes overwrought with the limited tools of test. Additionally, test results may take a long time to acquire, leaving behind a higher potential for the prevalence of the virus among other individuals by the patients. To reduce the chances of infection, we suggest a prediction model that distinguishes the infected COVID-19 cases based on clinical symptoms and features. This model can be helpful for citizens to catch their infection without the need for visiting the hospital. Also, it helps the medical staff in triaging patients in case of a deficiency of medical amenities. In this paper, we use the non-dominated sorting genetic algorithm (NSGA-II) to select the interesting features by finding the best trade-offs between two conflicting objectives: minimizing the number of features and maximizing the weights of selected features. Then, a classification phase is conducted using an AdaBoost classifier. The proposed model is evaluated using two different datasets. To maximize results, we performed a natural selection of hyper-parameters of the classifier using the genetic algorithm. The obtained results prove the efficiency of NSGA-II as a feature selection algorithm combined with AdaBoost classifier. It exhibits higher classification results that outperformed the existing methods. [ABSTRACT FROM AUTHOR]

The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed., Competing Interests: Conflict of interestThe authors declare that there is no conflict of interest with any person(s) or Organization(s)., (© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.)