WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar, Janga Vijay; Harshavardhan, A.; Bhukya, Hanumanthu; Prasad, Ashwani

doi:10.1016/j.matpr.2020.10.053

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Finally, Harshavardhan, Bhukya [23] performed the analysis of key points from medical images of the COVID-19 dataset using Generative Adversarial Network (GAN) architecture. The patterns and key points of the medical images can be analyzed using GANs.…”

Section: Deep Learning-basedmentioning

confidence: 99%

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Rahmani

Azhir

Naserbakht

et al. 2022

Multimed Tools Appl

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Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learningbased, active learning-based, transfer learning-based, and evolutionary learningbased mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.

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Section: Deep Learning-basedmentioning

confidence: 99%

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Rahmani

Azhir

Naserbakht

et al. 2022

Multimed Tools Appl

View full text Add to dashboard Cite

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Prioritizing and queueing the emergency departments’ patients using a novel data-driven decision-making methodology, a real case study

Alipour-Vaezi¹,

Aghsami²,

Jolai³

2022

Expert Systems with Applications

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One of the principal problems in epidemic disruptions like the COVID-19 pandemic is that the number of patients needing hospitals’ emergency departments’ services significantly grows. Since COVID-19 is an infectious disease, any aggregation has to be prevented accordingly. However, few aggregations cannot be prevented, including hospitals. To the best of our knowledge, COVID-19 is a life-threatening disease, especially for people in poor health conditions. Therefore, it sounds reasonable to optimize the health care queuing systems to minimize the infection rate by prioritizing patients based on their health condition so patients with a higher risk of infection will leave the queue sooner. In this paper, relying on data mining models and expert’s opinions, we propose a method for patient classification and prioritizing. The optimal number of servers (treatment systems) will be determined by benefiting from a mixed-integer model and the grasshopper optimization algorithm.

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Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks

Hongyun

Li²,

Sun³

et al. 2022

Sci Rep

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Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors’ intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806–0.811), accuracy: 0.815 (0.812–0.818), precision: 0.840 (0.835–0.845), and F1 score of XGBoost: 0.843 (0.840–0.845) and recall of SVM: 0.991 (0.988–0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

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WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Cited by 6 publications

References 19 publications

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Prioritizing and queueing the emergency departments’ patients using a novel data-driven decision-making methodology, a real case study

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks

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