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COVID-19 is a new variant of SARS-COV-2 which can lead to mild to severe infection in humans. Despite the remarkable efforts to contain the epidemic, the virus spread rapidly around the world and its prevalence continued with different degrees of clinical symptoms in many countries. Although common strategies including prevention, diagnosis, and care are necessary to curb this epidemic, early and accurate diagnosis can play an important role in reducing the speed of the epidemic. In this regard, the use of technologies based on artificial intelligence can be of great help. For this reason, since the outbreak of COVID-19, many researchers have tried to use machine learning techniques as a subset of artificial intelligence for the early diagnosis of COVID-19. Considering the importance and role of using clinical and laboratory data in the diagnosis of people with covid-19, in this paper K-NN, SVM, decision tree, random forest, Naive Bayes, neural network and XGBoost models are the most common machine learning models, and a dataset containing 1354 records consisting of clinical and laboratory data of patients in Imam Hossein Hospital in Tehran has been used to diagnose patients with covid-19. The results of this research indicate that based on the evaluation criteria, XGBoost and K-NN models have the most accuracy among the mentioned models and can be considered suitable predictive models for the diagnosis of COVID-19.

Today, due to the large volume of data and the high speed of data production, it is practically impossible to analyze data using traditional methods. Meanwhile, data mining, as one of the most popular topics in the present century, has contributed to the advancement of science and technology in a number of areas. In the recent decade, researchers have made extensive use of data mining to analyze data. One of the most important issues for researchers in this field is to identify common mainstreams in the fields of data mining and to find active research fields in this area for future research. On the other hand, the analysis of social networks in recent years as a suitable tool to study the present and future relationships between the entities of a network structure has attracted the researcher’s scrutiny. In this paper, using the method of co-occurrence analysis of words and analysis of social networks, the scientific structure and map of data mining issues in Iran based on papers indexed during the years 1388 to 1398 in the Civilica database is drawn, and the thematic trend governing research in this area has been reviewed. The results of the analysis show that in the category of data mining, concepts such as clustering, classification, decision tree, and neural network include the largest volume of applications such as data mining in medicine, fraud detection, and customer relationship management have had the greatest use of data mining techniques.

Collaboration in writing scientific articles with the growth of academic exchanges and social interactions of researchers is increasingly expanding. Scientific collaboration gives researchers the opportunity to combine the capabilities and abilities of different scientific and research disciplines, which cannot be done individually. Co-authorship is the most formal manifestation of intellectual collaboration between authors in the production of scientific research. On the other hand, the study of the trend of scientific activities and its dynamics in any specialized field is one of the most important concerns of researchers in that field. In recent years, the use of the social network analysis approach has been proposed as a suitable solution to map the scientific structure of specialized fields and the co-authorship network of researchers. In this research, the papers published in six web research conferences have been analyzed to discover the scientific network and the co-authorship based on the social network analysis approach. The results of the analysis show that in the period, concepts such as social network analysis, Internet of Things, cloud computing, and deep learning have the largest share in articles. Also, based on the number of communities formed, the authors of the conference papers were more inclined to form small scientific groups in the form of universities or research institutes of their respective organizations.