scholarly journals Analysis of Covid-19 in the United States using Machine Learning

2020 ◽  
Vol 8 (1) ◽  
pp. 15-21
Author(s):  
James G. Koomson
Keyword(s):  
United States ◽  
Machine Learning ◽  
The United States ◽  
Global Level ◽  
Learning Models ◽  
The World ◽  
Day By Day ◽  
Machine Learning Models

The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none ever seen before this century. Its impact has been massive on a global level. The deadly virus has commanded nations around the world to increase their efforts to fight against the spread of the virus after the stress it has put on resources. With the number of new cases increasing day by day around the world, the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning models to understand its behavior and predict future patterns in the United States (US) based on data obtained from the COVID-19 Tracking Project.

scholarly journals Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

2020 ◽  
Author(s):  
Piyush Mathur ◽  
Tavpritesh Sethi ◽  
Anya Mathur ◽  
Kamal Maheshwari ◽  
Jacek B Cywinski ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Random Forest ◽  
Population Density ◽  
The United States ◽  
Median Income ◽  
Relative Importance ◽  
Learning Models ◽  
Categorical Models ◽  
Machine Learning Models

AbstractBackgroundCOVID-19 is now one of the leading causes of mortality amongst adults in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations.ObjectiveThe goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel, and health care characteristics of different states across the United States and COVID-19 mortality.MethodsUsing multiple public data sets, 24 variables linked to COVID-19 disease were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States.ResultsFeature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age,median income, climate and disease mitigation measures on COVID-19 related mortality remained unclear.ConclusionsCOVID-19 policy making will need to take population density, pre-existing medical care and state travel policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.

scholarly journals Forecasting American COVID-19 Cases and Deaths through Machine Learning

2020 ◽  
Author(s):  
Anaiy Somalwar
Keyword(s):  
United States ◽  
Machine Learning ◽  
Curve Fitting ◽  
The United States ◽  
Learning Model ◽  
Learning Models ◽  
Gaussian Curve ◽  
Machine Learning Model ◽  
Gradient Based ◽  
Machine Learning Models

COVID-19 has become a great national security problem for the United States and many other countries, where public policy and healthcare decisions are based on the several models for the prediction of the future deaths and cases of COVID-19. While the most commonly used models for COVID-19 include epidemiological models and Gaussian curve-fitting models, recent literature has indicated that these models could be improved by incorporating machine learning. However, within this research on potential machine learning models for COVID-19 forecasting, there has been a large emphasis on providing an array of different types of machine learning models rather than optimizing a single one. In this research, we suggest and optimize a linear machine learning model with a gradient-based optimizer for the prediction of future COVID-19 cases and deaths in the United States. We also suggest that a hybrid of a machine learning model for shorter range predictions and a Gaussian curve-fitting model or an epidemiological model for longer range predictions could greatly increase the accuracy of COVID-19 forecasting.

Forecasting American COVID-19 Cases and Deaths through Machine Learning (Preprint)

2020 ◽  
Author(s):  
Anaiy Somalwar
Keyword(s):  
United States ◽  
Machine Learning ◽  
Curve Fitting ◽  
The United States ◽  
Learning Model ◽  
Learning Models ◽  
Gaussian Curve ◽  
Machine Learning Model ◽  
Gradient Based ◽  
Machine Learning Models

UNSTRUCTURED COVID-19 has become a great national security problem for the United States and many other countries, where public policy and healthcare decisions are based on the several models for the prediction of the future deaths and cases of COVID-19. While the most commonly used models for COVID-19 include epidemiological models and Gaussian curve-fitting models, recent literature has indicated that these models could be improved by incorporating machine learning. However, within this research on potential machine learning models for COVID-19 forecasting, there has been a large emphasis on providing an array of different types of machine learning models rather than optimizing a single one. In this research, we suggest and optimize a linear machine learning model with a gradient-based optimizer for the prediction of future COVID-19 cases and deaths in the United States. We also suggest that a hybrid of a machine learning model for shorter range predictions and a Gaussian curve-fitting model or an epidemiological model for longer range predictions could greatly increase the accuracy of COVID-19 forecasting. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1101/2020.08.13.20174631

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

Conclusion

2020 ◽  
pp. 97-102
Author(s):  
Benjamin Wiggins
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
The United States ◽  
Blaise Pascal ◽  
Division Problem ◽  
The World

Can risk assessment be made fair? The conclusion of Calculating Race returns to actuarial science’s foundations in probability. The roots of probability rest in a pair of problems posed to Blaise Pascal and Pierre de Fermat in the summer of 1654: “the Dice Problem” and “the Division Problem.” From their very foundation, the mathematics of probability offered the potential not only to be used to gain an advantage (as in the case of the Dice Problem), but also to divide material fairly (as in the case of the Division Problem). As the United States and the world enter an age driven by Big Data, algorithms, artificial intelligence, and machine learning and characterized by an actuarialization of everything, we must remember that risk assessment need not be put to use for individual, corporate, or government advantage but, rather, that it has always been capable of guiding how to distribute risk equitably instead.

scholarly journals Extra Point Under Review: Machine Learning And The NFL Field Goal

Elements ◽  
2016 ◽  
Vol 12 (2) ◽  
Author(s):  
James LeDoux
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Learning Models ◽  
The World ◽  
Extra Point ◽  
Machine Learning Models

<p>The new NFL extra point rule first implemented in the 2015 season requires a kicker to attempt his extra point with the ball snapped from the 15-yard line. This attempt stretches an extra point to the equivalent of a 32-yard field goal attempt, 13 yards longer than under the previous rule. Though a 32-yard attempt is still a chip shot to any professional kicker, many NFL analysts were surprised to see the number of extra points that were missed. Should this really have been a surprise, though? Beginning with a replication of a study by Clark et. al, this study aims to explore the world of NFL kicking from a statistical perspective, applying econometric and machine learning models to display a deeper perspective on what exactly makes some field goal attempts more difficult than others. Ultimately, the goal is to go beyond the previous research on this topic, providing an improved predictive model of field goal success and a better metric for evaluating placekicker ability.</p>

scholarly journals COVID-19 Future Predictions Using 4 Supervised Machine Learning Models

2022 ◽  
pp. 61-66
Author(s):  
Aditi Vadhavkar ◽  
Pratiksha Thombare ◽  
Priyanka Bhalerao ◽  
Utkarsha Auti
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Supervised Machine Learning ◽  
Perioperative Outcomes ◽  
Support Vector ◽  
Learning Models ◽  
Death Rates ◽  
The World ◽  
Machine Learning Models

Forecasting Mechanisms like Machine Learning (ML) models having been proving their significance to anticipate perioperative outcomes in the domain of decision making on the future course of actions. Many application domains have witnessed the use of ML models for identification and prioritization of adverse factors for a threat. The spread of COVID-19 has proven to be a great threat to a mankind announcing it a worldwide pandemic throughout. Many assets throughout the world has faced enormous infectivity and contagiousness of this illness. To look at the figure of undermining components of COVID-19 we’ve specifically used four Machine Learning Models Linear Regression (LR), Least shrinkage and determination administrator (LASSO), Support vector machine (SVM) and Exponential smoothing (ES). The results depict that the ES performs best among the four models employed in this study, followed by LR and LASSO which performs well in forecasting the newly confirmed cases, death rates yet recovery rates, but SVM performs poorly all told the prediction scenarios given the available dataset.

scholarly journals A Brief Analysis of Key Machine Learning Methods for Predicting Medicare Payments Related to Physical Therapy Practices in the United States

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 57
Author(s):  
Shrirang A. Kulkarni ◽  
Jodh S. Pannu ◽  
Andriy V. Koval ◽  
Gabriel J. Merrin ◽  
Varadraj P. Gurupur ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Physical Therapy ◽  
Random Forest ◽  
Generalized Additive Model ◽  
Additive Model ◽  
The United States ◽  
Random Forest Regression ◽  
Key Variables ◽  
Machine Learning Models

Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments.

scholarly journals Identification of significant climatic risk factors and machine learning models in dengue outbreak prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia. Results This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks. Conclusions This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

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