WITHDRAWN: Evaluation of performance of an LR and SVR models to predict COVID-19 pandemic

Dharani, N; Bojja, Polaiah; Kumari, Pamula Raja

doi:10.1016/j.matpr.2021.02.166

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…2021 Brazil Brasil.io portal Daily confirmed cases till 6 June 2020 Linear Regression 11.42% - - - [ 38 ] Pernambuco (A state in brazil) 1.92% de Souza, D. G. S. et al., 2020 Amapa (A state in Brazil) Health surveillance secretary of Amapa Cumulative confirmed cases from 20 March to 31 August, 2020 Holt-Winters 162 - 0.98 0.34 [ 39 ] Dharani, N. P., et al. 2021 India Kaggle website Daily confirmed cases 30 January to 21 May 2020 Linear Regression 223.89 157.78 1.0 - [ 40 ] Doe, S. W., et al., 2020 USA Johns Hopkins University confirmed cases data for US counties Daily confirmed cases from 22 January to 31 May, 2020 and latitude, and longitude of each county CLEIR-Net 264.33 - - - [ 41 ] Fokas, A. S, et al., 2020 Italy European CDC website Daily confirmed cases Bidirectional LSTM network 538 - 0.9999 - [ 22 ] Spain 1022 0.9998 France 821 0.9997 Germany 1128 0.9997 USA 10754 0.9996 Sweden 178 0.9997 Ganiny, S., et al., 2020 India Worldometer website, India's Ministry of Health and Family Welfare, the Covindia website Daily confirmed, recovered, and deceased cases from 1 March to 25 July, 2020 ...…”

Section: Table S1mentioning

confidence: 99%

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Ghafouri‐Fard¹,

Mohammad‐Rahimi²,

Motie³

et al. 2021

Heliyon

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COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R 2 coefficient of determination (R 2 ), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R 2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R 2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

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Section: Table S1mentioning

confidence: 99%

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Ghafouri‐Fard¹,

Mohammad‐Rahimi²,

Motie³

et al. 2021

Heliyon

View full text Add to dashboard Cite

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“…In [26], COVID-19 cases were predicted through Support Vector Regression (SVR) using different nonlinearity structures. Compared to SVR with other kernel functions, SVR with a Gaussian kernel displayed better prediction.…”

Section: Related Workmentioning

confidence: 99%

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

Ismail¹,

Alsalamah²,

Mohamed³

2023

Computers, Materials &Amp; Continua

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As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent predictive method using stacked-based ensemble learning (GA-Stacking) optimized by a Genetic Algorithm (GA) for outbreak prediction and health decision aided processes. GA-Stacking utilizes five well-known classifiers, including Decision Tree (DT), Random Forest (RF), RIGID regression, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost), at its first level. It also introduces GA to identify comparisons to forecast the number, combination, and trust of these base classifiers based on the Mean Squared Error (MSE) as a fitness function. At the second level of the stacked ensemble model, a Linear Regression (LR) classifier is used to produce the final prediction. The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering, Johns Hopkins University, which consisted of 10,722 data samples. The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99% for the three selected countries. Furthermore, the proposed model achieved good performance when compared with existing baggingbased approaches. The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC, 2023, vol.74, no.2 to predict the epidemic trend for other countries when comparing preventive and control measures.

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“…Support vector machines and other models employing the kernel trick do not scale well to large numbers of training samples or large numbers of features in the input space, several approximations to the RBF kernel have been introduced. The commonly used kernel function is Radial Basis Function kernel (RBF) and it is expressed as follows 2 may be recognized as the squared Euclidean distance between the two feature vectors. The optimal hyper parameters in RBF kernel is (C, γ and ε) , where C is cost of constraints in the Lagrange formulation, ε is the insensitive-loss function and γ is the kernel parameter.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Robust Weighted Support Vector Regression Approach for Predictive Modeling

Muthukrishnan¹,

Kalaivani²

2023

IJST

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Objectives: To improve the robustness of Support Vector Regression (SVR), Robust Weighted Support Vector Regression (RWSVR) procedure has been proposed by using Hampel's weight function. It overcomes the drawback by adding weight to each sample observations. Methods: Regression analysis plays a vital role in many areas of science. In this article, the classical regression method, robust regression methods, Support Vector Regression and proposed Robust Weighted Support Vector Regression method are presented. A type of Support Vector Machine (SVM) known as Support Vector Regression (SVR) supports both linear and non-linear regression. This work mainly focuses on Support Vector Regression method. The datasets which are used for this study is StarsCYG, Carbonation and Prostate. Simulation study of three cases are considered with sample size 50,100 and 500 with the contamination levels of 0%, 5%, 10% and 20%. Findings: The most commonly used classical procedure is Least Squares, which is less efficient and very sensitive when the data contains outliers. This work mainly focuses on increasing the accuracy of Proposed method. It overcomes the drawback by adding weight to each sample observations. The efficiency of the proposed method has been observed with the existing regression methods such as Least Squares (LS), Robust Linear Model (RLM) and Support Vector Regression (SVR) by computing various error measures such as Mean Absolute Error (MAE), Median Absolute Error (MDAE), Mean Absolute Percent Error (MAPE) and Root Mean Square Error (RMSE). The proposed RWSVR method is used for the researchers whenever the data contains outliers. Novelty: For getting more accuracy over the conventional SVR, robust Hampel's weight function is proposed. Newly developed function has a higher mapping power than the commonly used SVR function. The experimental results were carried out on real datasets and simulation study. It shows that the proposed RWSVR is significantly more robust than other regression methods.

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WITHDRAWN: Evaluation of performance of an LR and SVR models to predict COVID-19 pandemic

Cited by 11 publications

References 5 publications

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

Robust Weighted Support Vector Regression Approach for Predictive Modeling

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