Neural Network Based Country Wise Risk Prediction of COVID-19

Pal, Ratnabali; Sekh, Arif Ahmed; Kar, Samarjit; Prasad, Dilip K.

doi:10.3390/app10186448

Cited by 82 publications

(47 citation statements)

References 47 publications

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“…Pal et al ( 13 ), combined medical data with the trend and local weather data to forecast each country's level of risk. Specifically, a shallow LSTM neural network is employed in solving difficulties in limited datasets, and a country's risk level (high, medium, and recovery) is categorized using the Fuzzy rule.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

Kumar

Khan

Din

et al. 2021

Front. Public Health

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Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.

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Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

Kumar

Khan

Din

et al. 2021

Front. Public Health

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“…Yan et al [13] introduced an XGBoost classifier to predict the prognostic state's severe of COVID-19 infection using clinical data in Wuhan, China. In addition, Pal et al [14] introduced an LSTM model to predict the long duration outbreak caused by COVID-19 and how the risk affects the countries so they can take preventive steps earlier. On the other hand, Matteo et al [15] collected large dataset obtained from the social media platforms (e.g., Twitter, YouTube, Facebook) related to the COVID-19 and analyzed them.…”

Section: Machine Learning Based Techniquesmentioning

confidence: 99%

Transfer deep learning approach for detecting coronavirus disease in X-ray images

Al‐Smadi

Hammad

Baker

et al. 2021

IJECE

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<p><span lang="EN-US">Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task.</span></p>

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“…For instance, Kim et al [15] developed an epidemic disease spread and economic situation model based on LSTM to predict the economic impact of future COVID-19 spread. Pal et al [16] proposed an LSTM framework to predict a country-based COVID-19 risk category at a given time with a dataset from 180 countries. Zhang et al [17] used LSTM to reproduce soil stress-strain behavior, demonstrating better accuracy than other models.…”

Section: Literature Surveymentioning

confidence: 99%

Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison

Chu

Jang

2021

JMIR Med Inform

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Background Fatty liver disease (FLD) arises from the accumulation of fat in the liver and may cause liver inflammation, which, if not well controlled, may develop into liver fibrosis, cirrhosis, or even hepatocellular carcinoma. Objective We describe the construction of machine-learning models for current-visit prediction (CVP), which can help physicians obtain more information for accurate diagnosis, and next-visit prediction (NVP), which can help physicians provide potential high-risk patients with advice to effectively prevent FLD. Methods The large-scale and high-dimensional dataset used in this study comes from Taipei MJ Health Research Foundation in Taiwan. We used one-pass ranking and sequential forward selection (SFS) for feature selection in FLD prediction. For CVP, we explored multiple models, including k-nearest-neighbor classifier (KNNC), Adaboost, support vector machine (SVM), logistic regression (LR), random forest (RF), Gaussian naïve Bayes (GNB), decision trees C4.5 (C4.5), and classification and regression trees (CART). For NVP, we used long short-term memory (LSTM) and several of its variants as sequence classifiers that use various input sets for prediction. Model performance was evaluated based on two criteria: the accuracy of the test set and the intersection over union/coverage between the features selected by one-pass ranking/SFS and by domain experts. The accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve were calculated for both CVP and NVP for males and females, respectively. Results After data cleaning, the dataset included 34,856 and 31,394 unique visits respectively for males and females for the period 2009-2016. The test accuracy of CVP using KNNC, Adaboost, SVM, LR, RF, GNB, C4.5, and CART was respectively 84.28%, 83.84%, 82.22%, 82.21%, 76.03%, 75.78%, and 75.53%. The test accuracy of NVP using LSTM, bidirectional LSTM (biLSTM), Stack-LSTM, Stack-biLSTM, and Attention-LSTM was respectively 76.54%, 76.66%, 77.23%, 76.84%, and 77.31% for fixed-interval features, and was 79.29%, 79.12%, 79.32%, 79.29%, and 78.36%, respectively, for variable-interval features. Conclusions This study explored a large-scale FLD dataset with high dimensionality. We developed FLD prediction models for CVP and NVP. We also implemented efficient feature selection schemes for current- and next-visit prediction to compare the automatically selected features with expert-selected features. In particular, NVP emerged as more valuable from the viewpoint of preventive medicine. For NVP, we propose use of feature set 2 (with variable intervals), which is more compact and flexible. We have also tested several variants of LSTM in combination with two feature sets to identify the best match for male and female FLD prediction. More specifically, the best model for males was Stack-LSTM using feature set 2 (with 79.32% accuracy), whereas the best model for females was LSTM using feature set 1 (with 81.90% accuracy).

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Neural Network Based Country Wise Risk Prediction of COVID-19

Cited by 82 publications

References 47 publications

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

Transfer deep learning approach for detecting coronavirus disease in X-ray images

Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison

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