The use of Data Mining Techniques to Determine the İnfection with"Coved-19" in Iraq

Abstract

Since the emerging coronavirus pandemic spreads worldwide, countries continue to find new ways to combat and control the spread of"Covid-19."As part of the virus-fighting efforts, information on diagnostic procedures, infection and symptoms, and the most recent therapy and vaccine research have been updated. The main objective of this paper is to reduce the enormous load on the healthcare system by providing the best way to diagnose patients and predict the infection of COV-19 effectively. As a result of the scientific development in computers and their applications, this science has treated many medical problems. However, clinical trials and human skills are still required despite the undeniable contributions of artificial intelligence (AI) and data research responsible for fighting the pandemic. They are a global and open-source tool capable of assisting in this health emergency. However, due to the severity of the threat of this virus to global health and its rapid development, these solutions remain insufficient to combat it. This research paper uses data mining based on algorithms of AI and machine learning (ML) to detect and diagnose COVID-19 infection based on clinical diagnostic tests prepared previously in the Iraqi Ministry of Health. The model was provided with a dataset of the COVID-19 virus using the Python programming language. To create the model where the model predicts whether this person is infected or not infected with the virus, and if it is proven that he is in the danger zone (reaching death), can he bypass the virus and be cured. The current study results showed that the model was developed using the Random Forest Classifier algorithm more efficiently to predict infection with the Coronavirus. This represents the best model developed among other models that used various algorithms, such as Gaussian Naive Bayes, k-nearest neighbors, Support vector machine, Logistic Regression, Random Forest, Gradient boosting, Multi-layer Perceptron. © 2023 Author(s).