Performance Evaluation of Machine Learning Approaches for COVID-19 Forecasting by Infectious Disease Modeling

Khaloofi, Huda; Hussain, Jamil; Azhar, Zahra; Ahmad, Hafiz Farooq

doi:10.1109/widstaif52235.2021.9430192

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• There was a large mismatch between the real data and the typical SIR model. However, configuring the SIR model's parameters greatly enhanced the forecast [21]. • Parameter tuning significantly improved the prediction accuracy of the SIR and SEIQR models.…”

Section: Discussionmentioning

confidence: 99%

Implementation of Hybrid Prediction Model: An Unsupervised and Supervised Learning Perspective

Baig

Nayak

2023

IJRITCC

View full text Add to dashboard Cite

Using raw data to make inferences is the core of data science. This might be accomplished by closely examining the complex trends and patterns in the data. Machine Learning based forecasting methods have shown useful in predicting perioperative outcomes to improve the quality of future event planning decisions. In many application sectors where machine learning models were used, it has long been necessary to identify and prioritise the negative characteristics of a threat. Wish to provide precise predictions about a certain set of data, for example, use machine learning techniques in data science. Numerous prediction algorithms are now in use to address forecasting issues. Numerous epidemiological models are being employed internationally to forecast pandemic mortality rates and the number of affected people. Making the right decisions depends on the development of reliable prediction models. Epidemiological models have had trouble making longer-term forecasts with a higher degree of accuracy due to a lack of significant data and ambiguity. This research suggests a hybrid machine learning approach to anticipate the pandemic in contrast to Susceptible-Infected-Resistant based models, and we demonstrate its potential using COVID-19 data from India. Order to improve the identification of epidemics early, The model can also be updated using data from sources like search engine searches. Results from two well-known machine learning methods were compared to those from the improved SIR and SEIQR models.

show abstract

Section: Discussionmentioning

confidence: 99%

Implementation of Hybrid Prediction Model: An Unsupervised and Supervised Learning Perspective

Baig

Nayak

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…2022, 12, 10787 3 of 20 550 chest X-ray images from the Kaggle website and achieved a prediction accuracy of 89.7%. Huda Khaloofi et al [41] investigated various machine learning approaches to predict COVID-19 using online datasets. They explained the life cycle as well as the spread of COVID-19 disease in Saudi Arabia with associated performance analysis.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Ghosh

Banerjee²,

Das³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection.

show abstract

“…While these algorithms give good results for short term for extended periods most of these algorithms have low reliability. In [10] the author to overcome this drawback proposes a deep learning approach. The author investigate the life cycle and spread of COVID-19 in Saudi Arabia and Bahrain using this approach.…”

Section: B Data Driven Modelmentioning

confidence: 99%

Epidemic Modelling of COVID-19

Gupta¹

2021

IJRASET

View full text Add to dashboard Cite

COVID-19 has had a disastrous impact on millions of lives all over the world.199,466,211 confirmed cases of COVID19 and 4,244,541 deaths have been reported to WHO till 4th august. Analyzing the available data and predicting the pandemic trend is important since the situation can be controlled only when there is adequate preparation. Research using epidemiological models helps in analyzing different facets of COVID including infection, recovery and death rate. Predicting the daily increase of cases can help reduce the burden on health care workers and government by aiding them in planning the required resources in advance. Thus, in this project data driven epidemic modelling approach is used. COVID cases of 10 forthcoming days using three modelling techniques namely Polynomial Regression, Bayesian Ridge Regression and Support Vector Machine are predicted. The performance metric used to identify the best model are MSE and MAE. Polynomial Regression is found to have best performance followed by Bayesian ridge regression. Support Vector Machine has a poor performance. Keywords: Epidemic Modelling, COVID-19, Machine Learning, Polynomial Regression, Bayesian Ridge Regression, Support Vector Machine

show abstract

Performance Evaluation of Machine Learning Approaches for COVID-19 Forecasting by Infectious Disease Modeling

Cited by 5 publications

References 10 publications

Implementation of Hybrid Prediction Model: An Unsupervised and Supervised Learning Perspective

Implementation of Hybrid Prediction Model: An Unsupervised and Supervised Learning Perspective

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Epidemic Modelling of COVID-19

Contact Info

Product

Resources

About