Building predictive model for COVID-19 using  artificial neural network (ANN) algorithm

D. Christy Sujatha; P. Aruna; J. Jeyachidra

doi:10.53730/ijhs.v6nS2.6077

Authors

D. Christy Sujatha Assistant Professor, Department. of Software Engineering, Periyar Maniammai Institute of Science and Technology, Vallam, Thanjavur
P. Aruna Associate professor, Department of Software Engineering, Periyar Maniammai Institute of Science and Technology, Vallam, Thanjavur
J. Jeyachidra Professor, Department of Computer Science and Applications, Periyar Maniammai Institute of Science and Technology, Vallam, Thanjavur

Keywords:

COVID-19, machine learning algorithm, artificial neural network

Abstract

Machine learning plays an important role in addressing the pandemic crisis to analyse, identify and to forecast the infection and the spread of any contagious virus. Nowadays, most of the organizations and researchers are moving towards machine learning algorithms to develop predictive models, trying to reduce the death rate and to identify the patients who are at the increased risk of mortality. The major challenge of Covid-19 is, its identification and classification, due to the fact that the symptoms of Covid -19 are similar to other infectious diseases such as viral fever, typhoid, dengue, pneumonia and other lung infectious diseases. The objective of this paper is to build a predictive model for covid-19 using the Artificial Neural Network (ANN), a supervised machine learning Algorithm. In this study, the data set from Kaggle Sírio-Libanês data for AI and Analytics by the Data Intelligence Team has been used to build the predictive model. It is observed that there is 73% of accuracy in predicting the number of corona infected cases.

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https://www.kaggle.com/code/divendraseeram/covid-19-icu-prediction#Building-a-Predictive-Model-for-COVID-19-ICU-Admission