The Prediction of COVID-19 Using LSTM Algorithms

Kim, Myung Hwa; Kim, Ju Hyung; Lee, Kyoungjin; Gim, Gwang-Yong

doi:10.2991/ijndc.k.201218.003

Cited by 23 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This form of statistical reasoning is based on Bayes’ theorem, which enables us to update probabilities based on new evidence. In the context of optimization, particularly hyperparameter tuning, Bayesian inference emerges as a robust method to intelligently navigate the solution space [ 11 , 36 , 91 , 111 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…By enabling a nuanced balance between exploration and exploitation, Bayesian inference provides Bayesian Optimization with a level of sophistication unparalleled in traditional optimization algorithms. This intelligent balance allows Bayesian Optimization to adapt and excel, even in the most challenging optimization landscapes [ 36 , 53 , 88 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Economic consequences in both nations have led to reflection and restructuring, which may shape future growth patterns. This understanding forms the basis for further exploration of predictive models and the role of deep learning in healthcare, particularly in contexts defined by complexity and dynamism [ 16 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

View full text Add to dashboard Cite

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In various application domains, LSTM has proven to be the state-of-the-art sequence classifier that can achieve better results than classical methods. 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.…”

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

View full text Add to dashboard Cite

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).

show abstract

“…In addition, humanity has already lived through all of the year 2020 in a pandemic, so the collected data make it possible to compare 2020 with previous non-pandemic years. In particular, Kim et al [11] created an epidemic disease spread model and economic situation prediction model that are based on "long short-term memory" Morgan et al [12] analyzed the effects of COVID-19 on global economic output and sustainability. Kano et al [13] proposed an agent-based model of the interrelation between the COVID-19 outbreak and economic activities, and the impacts of the pandemic on energy demand [14] and natural gas consumption [15] were analyzed.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of COVID-19 spread in Europe using "center of gravity" concept

Yavorska¹,

Bun²

2022

Math. Model. Comput.

View full text Add to dashboard Cite

The COVID-19 global pandemic has affected all countries and become a real challenge for humanity. Scientists are intensively studying the specifics of the disease caused by this virus and the impact of restrictive measures on the economy, environment and other aspects of life. We present an approach to spatial modeling and analysis of the COVID-19 spreading process using the concept of the "center of gravity". Based on weekly data on this disease in all European countries, the trajectories of the center of gravity of new cases and deaths during the pandemic have been calculated. These two trajectories reflect the dominant role of certain countries or regions of Europe during different stages of the pandemic. It is shown that the amplitude of the trajectory of the center of gravity in the longitudinal direction was quite high (about 1,500 km) in comparison with the amplitude of the trajectory in the latitudinal direction (500 km). Using an approximation of the weekly data, the delays between the peaks of new cases and mortality for different countries were calculated, as well as the delays in comparison with the countries that first reached the peaks of morbidity and mortality. The trajectories of the center of gravity are also calculated for the regions of Ukraine as an example of analysis at the national scale. These results provide an opportunity to understand the spatial specifics of the spread of COVID-19 on the European continent and the roles of separate countries in these complex processes.

show abstract

The Prediction of COVID-19 Using LSTM Algorithms

Cited by 23 publications

References 15 publications

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

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

Spatial analysis of COVID-19 spread in Europe using "center of gravity" concept

Contact Info

Product

Resources

About