Deep Analysis And Theoretical Investigation Of Covid-19 Pandemic In Iraq Using Data Mining Techniques - et. al., Atheer Y.O. Allmuttar, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The aim of this paper is to deeply analyze the Corona-Virus Diseases (Covid-19) using data mining based K-Means Clustering technique. Medical Science in data mining is an emerging field that has proposed a lot of advanced techniques in analysis of a particular disease. Treatment of coronavirus is getting more and more challenging due to complex structure, shape and texture of the virus. Therefore, by advancing in data mining, K-Means methodology has been proposed to analyze the covid-19s in the world. The advancement in this field created an urge in me to research more on the techniques and methodologies developed for covid-19 extraction. During the outbreak of an epidemic, it is of immense interest to monitor the effects of containment measures and forecast of outbreak including epidemic peak. To confront the epidemic, a simple K-Means model is used to simulate the number of affected patients of Coronavirus disease in Iraq. The inhibition effect or precautionary measures also influence the spreading of a pandemic. If the inhibition factor increases up to 50%, then 0.5 million patients will be existing in Iraq till the end of this year. This number will exceed 1 million, if precautionary measures decrease to 50%. The worst effects of the disease appear in the community if we remove all the barriers. In such a case, this malady may increase by affecting 55% population till the end of this month. This number will start to decrease after September.