新冠肺炎疫情的随机森林算法预测和电子医疗数据分析

Debabrata Dansana, Raghvendra Kumar, Aishik Bhattacharjee, Chandrakanta Mahanty
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引用次数: 1

摘要

预测模型采用随机森林算法。从结果来看,回归模型利用了基本的联系原理,对于不同国家以及印度的COVID-19病例的预测非常可靠。以世界人口为基础,预测了目前全球新冠肺炎确诊病例的比例,并对包括美国在内的10个疫情最严重国家的新冠肺炎总病例增长进行了比较研究。预测了COVID-19确诊病例与死亡病例之间的比例,最后对印度进行了一项特别研究,我们预测了受COVID-19影响的所有年龄组,然后我们将研究扩展到预测活跃病例、死亡病例和康复病例,特别是在印度,并将情况与其他国家进行了比较。
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COVID-19 Outbreak Prediction and Analysis of E-Healthcare Data Using Random Forest Algorithms
The forecasting model used random forest algorithm. From the outcomes, it has been found that the regression models utilize basic linkage works and are exceptionally solid for forecast of COVID-19 cases in different countries as well as India. Current shared of worldwide COVID-19 confirmed case has been predicted by taking the world population and a comparatives study has been done on COVID-19 total cases growth for top 10 worst affected countries including US and excluding US. The ratio between confirmed cases vs. fatalities of COVID-19 is predicted and in the end a special study has been done on India where we have forecasted all the age groups affected by COVID-19 then we have extended our study to forecast the active, death and recovered cases especially in India and compared the situation with other countries.
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