Journal of Computer and Communications, 2022, 10, 18-25 https://www.scirp.org/journal/jcc ISSN Online: 2327-5227 ISSN Print: 2327-5219 DOI: 10.4236/jcc.2022.108002 Aug. 12, 2022 18 Journal of Computer and Communications Framework Development Using Data Mining Techniques to Predict Mortality Risk during Pandemic Debjany Chakraborty, Md Musfique Anwar Computer Science and Engineering Department, Jahangirnagar University, Dhaka, Bangladesh 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 influ- enced 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 sustaina- ble approach for predicting the mortality risk during the pandemic to help medical decision making and raise public health awareness. This paper de- scribes the range of symptoms for corona virus suffered patients and the ways of predicting patient mortality rate based on their symptoms. Keywords Sequential forward Feature Selection, Symptom Categorization, Decision Tree, Attribute Selection Measure 1. Introduction The global impact of the novel coronavirus (COVID-19) requires accurate fore- casting 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 da- ta. At the same time, forecasts are highly influenced by the reliability of the data, vested interests, and what variables are being predicted. This paper introduces a How to cite this paper: Chakraborty, D. and Anwar, M.M (2022) Framework De- velopment Using Data Mining Techniques to Predict Mortality Risk during Pandemic. Journal of Computer and Communications, 10, 18-25. https://doi.org/10.4236/jcc.2022.108002 Received: January 23, 2022 Accepted: August 9, 2022 Published: August 12, 2022 Copyright © 2022 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access