COVID-19 Future Forecasting Using Supervised Machine Learning Models

Rustam, Furqan; Reshi, Aijaz Ahmad; Mehmood, Arif; Ullah, Saleem; On, Byung Won; Aslam, Waqar; Choi, Gyu Sang

doi:10.1109/access.2020.2997311

Cited by 455 publications

(297 citation statements)

References 22 publications

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“…In the work of [10], COVID-19 prediction models that use supervised ML was developed. The model was developed based on liner regression (LR), support vector machine (SVM), least absolute shrinkage and selection (LASSO), and exponential smoothing (ES) algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…A supervised ML approach, which Fig. 1 Flowchart for training process machine learning tasks [10] SN Computer Science incorporated the generic algorithm and weighted K-nearest neighbor (WKNN) algorithms to predict and classify DM type 2 according to the presence or absence of coronary artery disease complications, was developed in the work of [20]. The supervised ML predictive model for acute ischemic stroke post intra-arterial therapy was developed in the work of [21].…”

Section: Related Workmentioning

confidence: 99%

“…1, when training data are labeled, the training process is called supervised while otherwise it is called unsupervised training process. In contrast, when both labeled and unlabeled data are used for the training processing, this training process is called semi-supervised [10]. However, the task of supervised ML is the one most commonly used in real world applications, especially for the diagnosis and prediction of diseases.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Supervised Machine Learning Models for Diabetes Mellitus

2020

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Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively. Keywords Machine learning • Predictive model • Diabetes mellitus • Diabetes mellitus type 2 • Random forest This article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar".

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Supervised Machine Learning Models for Diabetes Mellitus

2020

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“…Vapnik [21] is the precursor of this technique, and its variant for regression, the support vector regression (SVR), which was widespread mainly by the work of Drucker et al [22]. Some applications of SVR can be found in the context of COVID-19 case forecasting [4,23,24].…”

Section: Svrmentioning

confidence: 99%

Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models

Souza

2020

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BACKGROUNDSince the first reports of COVID-19, decision-makers have been using traditional epidemiological models to predict the days to come. However, the enhancement of computational power, the demand for adaptable predictive frameworks, the short past of the disease, and uncertainties related to input data and prediction rules, also make other classical and machine learning techniques viable options.OBJECTIVEThis study investigates the efficiency of six models in forecasting COVID-19 confirmed cases with 17 days ahead. We compare the models autoregressive integrated moving average (ARIMA), Holt-Winters, support vector regression (SVR), k-nearest neighbors regressor (KNN), random trees regressor (RTR), seasonal linear regression with change-points (Prophet), and simple logistic regression (SLR).MATERIAL AND METHODSWe implement the models to data provided by the health surveillance secretary of Amapáa, a Brazilian state fully carved in the Amazon rainforest, which has been experiencing high infection rates. We evaluate the models according to their capacity to forecast in different historical scenarios of the COVID-19 progression, such as exponential increases, sudden decreases, and stability periods of daily cases. To do so, we use a rolling forward splitting approach for out-of-sample validation. We employ the metrics RMSE, R-squared, and sMAPE in evaluating the model in different cross-validation sections.FINDINGSAll models outperform SLG, especially Holt-Winters, that performs satisfactorily in all scenarios. SVR and ARIMA have better performances in isolated scenarios. To implement the comparisons, we have created a web application, which is available online.CONCLUSIONThis work represents an effort to assist the decision-makers of Amapá in future decisions to come, especially under scenarios of sudden variations in the number of confirmed cases of Amapá, which would be caused, for instance, by new contamination waves or vaccination. It is also an attempt to highlight alternative models that could be used in future epidemics.

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“…Rustam et al [5] studied different ML models to forecast the number of upcoming patients affected by the disease and included linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM) and exponential smoothing (ES). Three types of predictions are made namely, number of newly infected cases, the number of deaths and number of recoveries in the next 10 days.…”

Section: Related Workmentioning

confidence: 99%

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

Shetty

2020

Preprint

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COVID-19 is a pandemic that has caused lot of deaths and infections in the last 6 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP, an ANN model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine (ELM) to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function (PACF), which is a conventional method and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular nature-inspired optimization algorithm. The testing of the forecasting model has been done and comparison between the two parameter selection methods has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62 % on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4th March to 19th April 2020 has been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.

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COVID-19 Future Forecasting Using Supervised Machine Learning Models

Cited by 455 publications

References 22 publications

Predictive Supervised Machine Learning Models for Diabetes Mellitus

Predictive Supervised Machine Learning Models for Diabetes Mellitus

Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

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