Automatic COVID-19 prediction using explainable machine learning techniques

Solayman, Sanzida; Aumi, Sk. Azmiara; Mery, Chand Sultana; Mubassir, Muktadir; Khan, Riasat

doi:10.1016/j.ijcce.2023.01.003

Cited by 46 publications

(21 citation statements)

References 34 publications

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“…The assessment of the presented model CSJSO_Deep LSTM is examined with the preceding schemes like, Reinforcement learning [ 5 ], Auxiliary GAN [ 8 ], Deep learning [ 7 ] DNN [ 9 ], CSSA_Deep LSTM, and JSO_Deep LSTM in database-1 and Inf-Net [ 10 ], U-Net [ 1 ], Cascade CNN [ 11 ], Transfer learning [ 12 ], Auxiliary GAN [ 8 ], CSSA_Deep LSTM, and JSO_Deep LSTM in database-2. In the same way, the CSJSO_Deep LSTM is analyzed for routing with prior models, such as Fractional Artificial Bee Colony (FABC) [ 31 ], Multipath QoS Aware Routing Protocol (MMQARP) [ 32 ], Priority-based Congestion-avoidance Routing Protocol (PCRP routing) [ 33 ], Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) [ 34 ] and Autoregressive Squirrel Search (ArSS).…”

Section: Resultsmentioning

confidence: 99%

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

Amgothu,

Koppu

2023

PLoS ONE

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In the pandemic of COVID-19 patients approach to the hospital for prescription, yet due to extreme line up the patient gets treatment after waiting for more than one hour. Generally, wearable devices directly measure the preliminary data of the patient stored in capturing mode. In order to store the data, the hospitals require large storage devices that make the progression of data more complex. To bridge this gap, a potent scheme is established for COVID-19 prediction based fog-cloud named Caviar Squirrel Jellyfish Search Optimization (CSJSO). Here, CSJSO is the amalgamation of CAViar Squirrel Search Algorithm (CSSA) and Jellyfish Search Optimization (JSO), where CSSA is blended by the Conditional Autoregressive Value-at-Risk (CAViar) and Squirrel Search Algorithm (SSA). This architecture comprises the healthcare IoT sensor layer, fog layer and cloud layer. In the healthcare IoT sensor layer, the routing process with the collection of patient health condition data is carried out. On the other hand, in the fog layer COVID-19 detection is performed by employing a Deep Neuro Fuzzy Network (DNFN) trained by the proposed Remora Namib Beetle JSO (RNBJSO). Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). Finally, in the cloud layer, the detection of COVID-19 employing Deep Long Short Term Memory (Deep LSTM) trained utilizing proposed CSJSO is performed. The evaluation measures utilized for CSJSO_Deep LSTM in database-1, such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) observed 0.062 and 0.252 in confirmed cases. The measures employed in database-2 are accuracy, sensitivity and specificity achieved 0.925, 0.928 and 0.925 in K-set.

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Section: Resultsmentioning

confidence: 99%

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

Amgothu,

Koppu

2023

PLoS ONE

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“…The rapidly evolving disease and the straightforward transmission of virus pathogens have resulted in the development of numerous machine-learning models and applications. S. Solayman et al, in the study 9 , began by precisely preparing knowledge obtained from the Israeli Ministry of Health open-source website for classifiers. Experiments demonstrated that the hybrid convolutional neural network and long short-term memory algorithm with the SMOTE approach achieved the best results for classifying the introduced data.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Yakovyna,

Shakhovska,

Szpakowska

2024

Sci Rep

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Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.

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“…In addition, the Lagrangian relaxation approach and a constructive heuristic algorithm are considered to overcome the problem convolution and to solve large-scale instances. More studies regarding recent finding on COVID-19 can be found in ( Rai et al, 2022 , Solayman et al, 2023 ). Table 2 illustrates the distinctions in various features considered for supply chain networks among the papers analyzed.…”

Section: Survey On Related Researchmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

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Automatic COVID-19 prediction using explainable machine learning techniques

Cited by 46 publications

References 34 publications

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

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