A data mining analysis of COVID-19 cases in states of United States of America

Abstract

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

Antecedents of Online Purchasing Behavior for Products Marketing in Contemporary World: An Integrated Approach Withdeep Learning, Machine Learning and Data Mining

“…Conventional and unconventional forms of data gathering techniques can be applied as, sensor, user, service driven data collection or gathering, internet of things, paper administered surveys, online surveys, etc. (Yavuz, 2021a;Yavuz, 2021b;Yavuz, 2021c;Yavuz, 2022a;Yavuz, 2022b;Yavuz, 2022c;Yavuz, 2022d;Yavuz, 2022e).…”

“…Testing of the model with supervised and unsupervised versions of machine learning approaches takes place. Finally predicted analysis results are evaluated and assessed (Yavuz, 2021a;Yavuz, 2021b;Yavuz, 2021c;Yavuz, 2022a;Yavuz, 2022b;Yavuz, 2022c;Yavuz, 2022d;Yavuz, 2022e).…”

“…In the mapping process, independent multivariate variables are assigned to the respective class labels which are considered as the dependent variables in the output layer. In this context, a data mining approach strengthened with deep learning techniques has been employed to understand phenomena (Yavuz, 2021a;Yavuz, 2021b;Yavuz, 2021c;Yavuz, 2022a;Yavuz, 2022b;Yavuz, 2022c;Yavuz, 2022d;Yavuz, 2022e). The Data Mining methodology followed can be seen in Figure 1.…”

COVID-19 Social Lethality Characterization in Some Regions of Mexico through the Pandemic Years Using Data Mining

“…In a generic way, such works [25][26][27] used several supervised machine learning algorithms (classifiers) available in this tool for building classification models using COVID-19 datasets. Other relevant works used different strategies and tools: python programming language in developing data mining models for predicting COVID-19 infected patients' recovery using an epidemiological dataset of South Korea [28]; another generated its own dataset with the help of specialist physicians for predicting mortality in patients with COVID-19 based on data mining techniques [29]; another developed a model to predict the COVID-19 incidence rate in different regions of the world through a least-square classification algorithm [30]; another discovered rules on factors interrelated with COVID-19 pandemic using data mining methodologies [31], and one more used the RapidMiner Studio software [32] for creating a model to analyze and forecast the existence of COVID-19 using the so-called Kaggle dataset [33].…”

Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population

“…Many researchers established novel approaches for screening infected individuals at various stages to uncover noticeable connections between clinical variables and the likelihood of succumbing to the disease. In recent research investigations, artificial intelligence (AI) [7]- [9] and machine learning (ML) [10]- [12] approaches have been found to have a vital role in minimizing the impact of viral dissemination. The use of ML on patient data is being studied in various ways.…”