Predictive Modelling of COVID-19 New Cases in Algeria using An Extreme Learning Machines (ELM) (original) (raw)
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AIP Conference Proceedings
Covid-19 has spread to various countries in the world, including Indonesia. Surabaya becomes one of the big cities in Indonesia where the spread of Covid-19 is very fast, so the number of positive cases of Covid-19 is very large and more than 1000 people die because of this disease until November 2020. Prediction of the number of positive cases of Covid-19 can be used to regulate the availability of facilities in hospitals and make plans and policies to overcome this disease outbreak. Many prediction methods have been found, one of which is the Extreme Learning Machine (ELM). ELM has high training speed and accuracy. However, the performance of ELM depends on the number of neurons. When the number of neurons is not precisely determined, the accuracy of prediction becomes worst. Particle Swarm Optimization (PSO) is used to decide the number of neurons. For this reason, this paper proposes a prediction of the Covid-19 cases in the City of Surabaya using the hybrid of ELM and PSO (ELM-PSO). The experiments show that the comparative performance of the proposed methods with several activation functions in the prediction of the Covid-19 cases in the City of Surabaya.
Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset
Computers, Materials & Continua
Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology dataset. In the proposed MOKELM-CPED model, the data first undergoes pre-processing to transform the medical data into useful format. Followed by, data classification process is performed by following Kernel Extreme Learning Machine (KELM) model. Finally, Symbiotic Organism Search (SOS) optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection efficiency. In order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner, a comprehensive experimental analysis was conducted and the results were inspected under diverse aspects. The outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.
Predictive modelling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network
This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC).Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models.The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.
Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection
PLOS ONE, 2020
The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) ...
Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases
SN Computer Science
An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2 , RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead. Keywords Time-series forecasting • COVID-19 • Long Short-Term Memory (LSTM) • Autoregressive integrated moving average (ARIMA) • Exponential smoothing (ETS) • Artificial neural network (ANN) • Gene expression programming (GEP)
Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset
SN Computer Science, 2020
COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.
Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia
Computers, Materials & Continua
In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation-Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN-PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.
Prediction of COVID-19 Cases using Machine Learning for Effective Public Health Management
Computers, Materials & Continua
COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MachineLearning-BasedModel toPredict theDiseaseSeverity and Outcome in COVID-19 Patients
2021
e novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities. Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily. A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate. is research provides a prediction method for the early identification of COVID-19 patient's outcome based on patients' characteristics monitored at home, while in quarantine. e study was performed using 287 COVID-19 samples of patients from the King Fahad University Hospital, Saudi Arabia. e data were analyzed using three classification algorithms, namely, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). Initially, the data were preprocessed using several preprocessing techniques. Furthermore, 10-k cross-validation was applied for data partitioning and SMOTE for alleviating the data imbalance. Experiments were performed using twenty clinical features, identified as significant for predicting the survival versus the deceased COVID-19 patients. e results showed that RF outperformed the other classifiers with an accuracy of 0.95 and area under curve (AUC) of 0.99. e proposed model can assist the decision-making and health care professional by early identification of at-risk COVID-19 patients effectively.
Prediction Active Case of Covid-19 with ERNN
JTAM (Jurnal Teori dan Aplikasi Matematika), 2022
SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability.