期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Framework Development Using Data Mining Techniques to Predict Mortality Risk during Pandemic
1
作者 Debjany Chakraborty Md Musfique Anwar 《Journal of Computer and Communications》 2022年第8期18-25,共8页
The corona virus, which causes the respiratory infection Covid-19, was first detected in late 2019. It then spread quickly across the globe in the first months of 2020, reaching more than 15 million confirmed cases by... The corona virus, which causes the respiratory infection Covid-19, was first detected in late 2019. It then spread quickly across the globe in the first months of 2020, reaching more than 15 million confirmed cases by the second half of July. This global impact of the novel coronavirus (COVID-19) requires accurate forecasting about the spread of confirmed cases as well as continuation of analysis of the number of deaths and recoveries. Forecasting requires a huge amount of data. At the same time, forecasts are highly influenced by the reliability of the data, vested interests, and what variables are being predicted. Again, human behavior plays an important role in efficiently controling the spread of novel coronavirus. This paper introduces a sustainable approach for predicting the mortality risk during the pandemic to help medical decision making and raise public health awareness. This paper describes the range of symptoms for corona virus suffered patients and the ways of predicting patient mortality rate based on their symptoms. 展开更多
关键词 sequential forward Feature selection Symptom Categorization Decision Tree Attribute selection Measure
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部