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Anwar, M.M., Liu, C. and Li, J. (2018) Uncovering Attribute-Driven Active Intimate Communities. In: Wang, J., Cong, G., Chen, J. and Qi, J., Eds., Databases Theory and Applications. ADC 2018, Springer, Cham, 109-122.
https://doi.org/10.1007/978-3-319-92013-9_9

has been cited by the following article:

• TITLE: Framework Development Using Data Mining Techniques to Predict Mortality Risk during Pandemic

  AUTHORS: Debjany Chakraborty, Md Musfique Anwar

  KEYWORDS: Sequential forward Feature Selection, Symptom Categorization, Decision Tree, Attribute Selection Measure

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.8, August 12, 2022

  ABSTRACT: 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.