Efficient COVID Prediction Based on Hybrid Classification in Data Mining

Abstract

Since December 2019, there is a great pandemic situation is obtained in all over the world named as novel corona virus or COVID-19.  This was very firstly stated in end of 2019 December and suddenly it is affected more than 7 million humans. The report stated that some of the COVID infected peoples are lost their lives over 0.40 million approximately. India is also most affected country by COVID where first cases is reported at on 30 January 2020 in India and also crossed more than a millions of people in both first and second wave. This work is to develop a detailed analysis of spreading rate and Death rate of COVID in india based on the datasets. Therefore to achieve an accurate solution in the prediction, the Deep Learning (DL) classification techniques are proposed in this work. In this work, hybrid MobileNetDNN and Decision Tree (MDT) model is proposed. This hybrid classification technique is used to achieve a high COVIDspread rate and Mortality Rate precision. The outcome of proposed MDT model is compared with the prior model based on the parameters of specificity, Accuracy, Recall and precision respectively.