1. Clustering endemic places of Covid-19 by using K-mean.
- Author
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Mushib, Safa Mohammed and Ali, Israa Tahseen
- Subjects
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COVID-19 pandemic , *MACHINE learning , *COVID-19 , *K-means clustering , *COMMUNICABLE diseases , *VIRAL transmission - Abstract
Today, the world is exposed to many widespread health crises. One of these crises is the emergence and spread of the covid-19 virus, where any person becomes infected with this disease through the rapid transmission of infection between people, either by palpation or by inhaling droplets of carriers of this virus. Hence, there is a need to build a tracking system to respond quickly to problems. This paper proposes using one of the unsupervised machine learning algorithms, the K-Means Clustering Data, to determine the areas with the most significant percentage of infections by dividing them into infected and uninfected human groups. Where the infected persons are divided into three high groups (C1), medium (C2), and low (C3), and follow the path of the infected persons using Google Maps. The contribution to this paper is to support appropriate decision-making by public health authorities by predicting endemic areas and how to address problems that occur as they can use this system to reduce other infectious diseases. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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