Covid-19 Patients Diagnosis (CPD) Strategy Using Data Mining Techniques

Authors

Alaa Mohamed, Master Degree Researcher of Electronics and Communication Department, Faculty of Engineering, Mansoura University works at Delta Higher Institute for Engineering and TechnologyFollow
Ahmed Saleh, Professor at the Computers and Control Department, Faculty of Engineering, Mansoura University, Egypt.Follow
Doaa A. Altantawy, Assistant Professor at the Electronics and Communication Department, Faculty of Engineering, Mansoura University, EgyptFollow
Mohy Eldin Abo-Elsoud, Professor at the Electronics and Communication Department, Faculty of Engineering, Mansoura University, EgyptFollow

Corresponding Author

Mohamed, Alaa

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

Covid-19, the world continues to live in anxiety and instability despite efforts to find a vaccine and emerge from this crisis. Especially after the emergence of a new mutated Corona virus called Omicron. This mutated sparked a state of controversy about the extent of its impact and its ability to spread among people. The Covid-19 epidemic has thrown the world economy into disarray. It also resulted in a widespread suspension of work and output throughout society, hurting economic and society. This paper introduces a Covid-19 Patients Diagnosis (CPD) strategy that works to find a fast and highly effective prognosis for diagnosing Covid-19 patients. The proposed strategy has two main stages named Feature Selection Stage (FSS) and Covid-19 Diagnosis Stage (CDS). The FSS has main objective to select the powerful features for the diagnosis stage. The features are selected in the FSS by using Chi-Square Feature Selection (CSFS) method. In fact, CSFS is a filter feature selection technique that has the ability to quickly choose the most effective subset of features. Then, quick and accurate diagnosis is provided by using Improved K-Nearest Neighbors (IKNN). The main idea in the proposed IKNN is that a circle with a radius value that equals the average distance of K of the closet items will be constructed and then the nearest M of items will be determined to classify the patient to the correct class “Covid” or “Non-Covid”. The results explain that the proposed strategy called CPD gives an accuracy of up to 96.36%.

Recommended Citation

Mohamed, Alaa; Saleh, Ahmed; A. Altantawy, Doaa; and Abo-Elsoud, Mohy Eldin (2022) "Covid-19 Patients Diagnosis (CPD) Strategy Using Data Mining Techniques," Mansoura Engineering Journal: Vol. 47 : Iss. 2 , Article 5.
Available at: https://doi.org/10.21608/bfemu.2022.233811