Using data mining techniques deep analysis and theoretical investigation of COVID-19 pandemic

https://doi.org/10.1016/j.measen.2023.100747Get rights and content
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Abstract

This study uses K-Means Clustering to analyze Corona-Virus Diseases (Covid-19). Data mining in medicine has generated novel approaches to examine diseases. Coronavirus is difficult to treat because of its intricate structure, shape, and texture. Due to data mining improvements, the K-Means approach has been developed for evaluating covid-19. Observe the outbreak's evolution, including its peak, and containment measures. A basic K-Means model is used to simulate Coronavirus's prevalence in Iraq. Pandemic-prevention efforts may slow its spread. If inhibition grows to 50%, Iraq will have 500,000 patients by year's end. If precautions were halved, the number would top 1 million. If we abandon all measures, the sickness will worsen. In that case, 55% of the population may be affected by the end of the month. This number will drop after September.

Keywords

Data mining
K-means
Clustering
Reported cases
Analysis

Data availability

No data was used for the research described in the article.

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