Classification and Analysis of COVID-19 Clinical Big Data using Machine Learning Models

Research areas:

• Volume 11,Issue 2, Sept. 2022

Year:

2022

Type of Publication:

Article

Keywords:

COVID-19 Clinical Data, Principal Component Analysis, Classification, Random Forest, Gadient Boosted Trees, Support Vector Machine

Authors:

M. M. Lotfy; Hazem M. El-Bakry; M. M. El-Gayar; A. A. Soliman

Journal:

IJAIM

Volume:

11

Number:

2

Pages:

25-33

Month:

September

ISSN:

2320-5121

Abstract:

We are in the digital transformation era, and through the availability of big data, we can make the appropriate decision in the fastest time by analyzing this huge data. Because of the rapid and widespread of the Covid-19 epidemic, big data of the Covid-19 disease and the infection data have been available that can be analyzed to limit the spread of the disease. In this paper, multiple models of different machine learning algorithms will be applied to large clinical data to classify whether they have Covid-19 disease or not. The data was first cleaned and then the importance of features for clinical data was measured as a preliminary data processing before the classification stage. In the experiments, classification and prediction accuracy of more than 92% was achieved.

Full text: IJAIM_660_FINAL.pdf