Covid-19 Analysis and Prediction using Data Science and Machine Learning

Abstract: Officials around the world are using several COVID-19 outbreak prediction models to make educated decisions and enact necessary control measures. In this study, we developed a Machine Learning model which predicts and forecasts the COVID-19 outbreak in India, with the goal of determining the best regression model for an in-depth examination of the novel coronavirus. Based on data available from January 31 to October 31, 2020, collected from Kaggle, this model predicts the number of confirmed cases in Maharashtra. We're using a Machine Learning model to foresee the future trend of these situations. The project has the potential to demonstrate the importance of information dissemination in improving response time and planning ahead of time to help reduce risk.

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The Potential of "numerical approaches" in banking supervision: Possibility of proactive identification by the bank of Russia for problem credit institutions in the banking sector using data science methods

KANT ◽

10.24923/2222-243x.2020-37.44 ◽

2020 ◽

Vol 37 (4) ◽

pp. 205-209

Author(s):

Anastasiia Sterlikova

Keyword(s):

Machine Learning ◽

Data Science ◽

Banking Sector ◽

Learning Model ◽

Science Methods ◽

Banking Supervision ◽

Machine Learning Model ◽

Using Data ◽

Numerical Approaches ◽

Credit Institutions

The article discusses the possibility of machine learning model for analyzing the state of credit institutions by their performance indicators and assessing the likelihood of revoking a license from a single participant. The conclusion is made about the possibility of using the machine learning model in the supervisory activities of the Bank of Russia as an auxiliary tool.

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Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i08.20099 ◽

2021 ◽

Vol 17 (08) ◽

pp. 154

Author(s):

Shakir Khan

Keyword(s):

Machine Learning ◽

Saudi Arabia ◽

Prediction Models ◽

Preventive Measures ◽

Future Trend ◽

Control Measures ◽

World Health ◽

Seir Model ◽

The World ◽

Health Organization

<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

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Quantitative Toxicity Prediction via Ensembling of Heterogeneous Predictors

10.21203/rs.2.19338/v1 ◽

2019 ◽

Author(s):

Abdul Karim ◽

Vahid Riahi ◽

Avinash Mishra ◽

Abdollah Dehzangi ◽

M. A. Hakim Newton ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Models ◽

Individual Performance ◽

Learning Model ◽

Data Representation ◽

Toxicity Prediction ◽

Machine Learning Model ◽

Machine Learning Approach ◽

Benchmark Datasets

Abstract Representing molecules in the form of only one type of features and using those features to predict their activities is one of the most important approaches for machine-learning-based chemical-activity-prediction. For molecular activities like quantitative toxicity prediction, the performance depends on the type of features extracted and the machine learning approach used. For such cases, using one type of features and machine learning model restricts the prediction performance to specific representation and model used. In this paper, we study quantitative toxicity prediction and propose a machine learning model for the same. Our model uses an ensemble of heterogeneous predictors instead of typically using homogeneous predictors. The predictors that we use vary either on the type of features used or on the deep learning architecture employed. Each of these predictors presumably has its own strengths and weaknesses in terms of toxicity prediction. Our motivation is to make a combined model that utilizes different types of features and architectures to obtain better collective performance that could go beyond the performance of each individual predictor. We use six predictors in our model and test the model on four standard quantitative toxicity benchmark datasets. Experimental results show that our model outperforms the state-of-the-art toxicity prediction models in 8 out of 12 accuracy measures. Our experiments show that ensembling heterogeneous predictor improves the performance over single predictors and homogeneous ensembling of single predictors.The results show that each data representation or deep learning based predictor has its own strengths and weaknesses, thus employing a model ensembling multiple heterogeneous predictors could go beyond individual performance of each data representation or each predictor type.

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Retail Giant Sales Forecasting using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1277.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 2408-2411

Keyword(s):

Machine Learning ◽

Data Mining ◽

Decision Making ◽

Learning Model ◽

Accurate Prediction ◽

Machine Learning Model ◽

Different Types ◽

Using Data ◽

Near Future ◽

Sales Trend

Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.

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Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

Scientific Reports ◽

10.1038/s41598-021-92475-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gregor Lichtner ◽

Felix Balzer ◽

Stefan Haufe ◽

Niklas Giesa ◽

Fridtjof Schiefenhövel ◽

...

Keyword(s):

Machine Learning ◽

Critically Ill ◽

Prediction Models ◽

Predictive Performance ◽

Learning Model ◽

Mortality Prediction ◽

Viral Pneumonia ◽

Machine Learning Model ◽

Mortality Prediction Models ◽

Time Courses

AbstractIn a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61–0.65]), SAPS2 (0.72 [95% CI 0.71–0.74]) and SOFA (0.76 [95% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95% CI 0.59–0.65]; clinical: 0.56 [95% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

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An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study (Preprint)

10.2196/preprints.24225 ◽

2020 ◽

Author(s):

Hyung-Jun Kim ◽

Deokjae Han ◽

Jeong-Han Kim ◽

Daehyun Kim ◽

Beomman Ha ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Prediction Models ◽

Discrimination Performance ◽

Learning Model ◽

Smoking History ◽

Case Report Form ◽

Validation Group ◽

Radiographic Findings ◽

Machine Learning Model

BACKGROUND Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. OBJECTIVE The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics—baseline demographics, comorbidities, and symptoms. METHODS A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. CONCLUSIONS We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

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Adaptive Hybrid Machine Learning Model For Forecasting The Step-Like Displacements of Reservoir Colluvial Landslides: A Case Study in The Three Gorges Reservoir Area, China

10.21203/rs.3.rs-217782/v1 ◽

2021 ◽

Author(s):

Li Linwei ◽

Yiping Wu ◽

Miao Fasheng ◽

Xue Yang ◽

Huang Yepiao

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Warning System ◽

Learning Model ◽

Model Complexity ◽

Three Gorges Reservoir Area ◽

Gray Wolf ◽

Displacement Prediction ◽

Machine Learning Model ◽

Hybrid Machine

Abstract Constructing an accurate and stable displacement prediction model is essential to build a capable early warning system for landslide disasters. To overcome the drawbacks of previous displacement prediction models for step-like landslides, such as the incomplete or excessive decompositions of cumulative displacements and input factors and the redundancy or lack of input factors, we propose an adaptive hybrid machine learning model. This model is composed of three parts. First, candidate factors are proposed based on the macroscopic deformation response of landslides. Then, the landslide displacement and its candidate factors are adaptively decomposed into different displacement and factor components by applying optimized variational mode decomposition (OVMD). Second, in the gray wolf optimizer-based kernel extreme learning machine (GWO-KELM) model, the global sensitivity analysis (GSA) of the prediction results of different displacement components to each decomposed factor is analyzed based on the PAWN method. Then, the decomposed factors are reduced according to the GSA results. Third, based on the reduced factors, the optimal GWO-KELM models of the different displacement components are established to predict the displacement. Taking the Baishuihe landslide as an example, we used the raw data of three representative monitoring sites from June 2006 to December 2016 to verify the validity, accuracy, and stability of the model. The results indicate that the proposed hybrid model can effectively determine the displacement decomposition parameters. In addition, this model performed well over a three-year forecast with low model complexity.

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An Advanced Machine Learning Model for Disease Prediction

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b4164.079220 ◽

2020 ◽

Vol 9 (2) ◽

pp. 1220-1225

Keyword(s):

Machine Learning ◽

Predictive Analytics ◽

Learning Model ◽

Control Measures ◽

Disease Prediction ◽

Common People ◽

Machine Learning Model ◽

The World ◽

Conventional Models ◽

Precision Rate

To settle on right choices and pass on about vital control measures, numerous flare-up expectation models for anticipating COVID-19 are getting utilized all round the world. Straightforward conventional models have indicated extremely less precision rate for future forecast use, because of more significant levels of vulnerability and absence of proper information. Among the different machine learning model algorithms contemplated, an ensembled model was seen as giving the best outcomes. Because of the multifaceted nature of the virus's temperament, this research paper recommends machine learning to be an extremely helpful gadget to consider in case of the ongoing pandemic. This paper gives a colossal benchmark to call attention to the probability of machine learning to be utilized as an instrument for future exploration on pandemic control and its timely prediction. Moreover, this paper delineates that the best prompts for pandemic prediction are frequently comprehended by combining machine learning, predictive analytics and visualisation tools like Tableau. The main purpose of this research is to build a perfect ML model prototype which can be later used when access to appropriate dataset (which is both large and consists of many different features) is available. Also, the secondary aim is to automate the process of reporting so as to facilitate quicker action by the concerned authorities, and help common people reach out to the correct destination for treatment or help. Furthermore, the Tableau analysis performed on the dataset is to provide more analytical depths for people with expertise in the medical domain.

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A machine learning model to predict death outcome in severe COVID-19 patients

10.21203/rs.3.rs-96621/v2 ◽

2021 ◽

Author(s):

Qiao Yang ◽

Jixi Li ◽

Zhijia Zhang ◽

Xiaocheng Wu ◽

Tongquan Liao ◽

...

Keyword(s):

Machine Learning ◽

Lactic Dehydrogenase ◽

Learning Model ◽

Severe Illness ◽

Decision Tree Classifier ◽

Risk Of Death ◽

Global Pandemic ◽

Machine Learning Model ◽

Tree Classifier ◽

Novel Coronavirus

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

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Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning

Bioengineering ◽

10.3390/bioengineering8050060 ◽

2021 ◽

Vol 8 (5) ◽

pp. 60

Author(s):

Sanjay Sarma Oruganti Venkata ◽

Amie Koenig ◽

Ramana M. Pidaparti

Keyword(s):

Machine Learning ◽

Mechanical Ventilation ◽

Prediction Models ◽

Particle Swarm ◽

Learning Model ◽

Good Prediction ◽

Machine Learning Model ◽

New Variant ◽

Target Values ◽

Lung Health

Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient’s survival once the relevant data are available.

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