A Data Mining Based Two-Staged Approach for COVID-19 Diagnosis

Document Type : Original Article

Authors

1 Cairo, Egypt

2 Computer Science Department, Faculty of Computers and Information Menoufia University, Egypt

Abstract

Because of the plague that threatens the entire planet, people continue to live in dread and instability. COVID-19 is considered one of the fastest pandemics that affected the whole world. As an initial step, PCR analysis is becoming more and more popular, although it has issues with accuracy. Using Convolutional neural network (CNN), it takes a while to identify a few computers tomography (CT) images despite its great accuracy in proper classification Additionally, classification using Data-Mining algorithms on (CT) images has been researched, and they achieve high accuracy in classification, but it is time-consuming. While scanning (CT) images, Patients are in danger when utilizing imaging equipment. Even after a deep sterilization, there is a major likelihood that the infection will persist on the scanning chamber surface.

This research presents a COVID-19 patient diagnosis approach apply to find a quickly and so effective classification using a blood test. The suggested approach consists of two main phases: a feature selection phase and covid-19 prediction phase. The feature selection is applied by implementing The Chi-Square feature selection technique. This methodology, a filter feature selection technique, can swiftly identify the most useful subset of features. Then, an improved support vector machine is used to deliver an immediate and accurate diagnosis (RBF SVM). The basic concept behind the suggested RBF SVM is choosing the value of hyperparameters, C and Y, in the supporting vector machine (SVM) formula before computing the SVM. The results demonstrate that the suggested method provides an accuracy of up to 98.8%.

Keywords

IJCI. International Journal of Computers and Information
Volume 10, Issue 3
Special issue for the proceedings of ICCI 2023 conference
November 2023
Pages 98-103

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