Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset

Document Type : Original Article


1 Department of Information Technology, Tarbiat Modares University, Tehran, Iran

2 Department of Internal Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Imam Hossein Hospital, Tehran, Iran


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.


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  •  Narges Mohebbi was born in 1996 in Tehran. She received her B.Sc. in Computer Engineering from Arak University in 2018. Currently, she is an M.Sc. student in Information Technology at Tarbiat Modares University. Her research interests include data mining and machine learning.
  •  Mehdi Tutunchian was born in 1996 in Tabriz. He received his B.Sc. in Information Technology Engineering from Tabriz University in 2018. Currently, he is an M.Sc. student in Information Technology at Tarbiat Modares University. His research interests include medical image processing and machine learning.
     Meysam Alavi was born in Hamadan, Iran. He received his B.Sc. and an M.Sc. degree in Computer Engineering in 2005 and 2010, respectively. Also, he received his Second M.Sc. degree in Information Technology at Tarbiat Modares University in 2020. His research interests include medical image processing, machine learning, data mining, and social network analysis.
  •  Mehrdad Kargari received his PhD degree in industrial engineering from Tarbiat Modares University of Iran. He is currently an assistant professor at the department of Information Engineering, in Tarbiat Modares University. His research interests are in the fields of machine learning, artificial intelligence, IoT and their applications in health or Banking.
  •  Amir behnam kharazmi was born in 1985. He is an internal medicine specialist, pulmonary subspecialist, and assistant professor at Shahid Beheshti medical university. Also, he is a member of the European Respiratory Society(ERS) and American thoracic society(ATS). His research interest is in the field of the application of artificial intelligence in medicine.