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

Document Type : Original Article

Authors

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

Abstract

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.

Keywords

References

  • Nayak, B. Naik, P. Dinesh, K. Vakula, B. K. Rao, W. Ding, and D. Pelusi. “Intelligent system for COVID-19 prognosis: A state-of-the-art survey”, Applied Intelligence, vol. 51, no. 5, pp. 2908-2938, 2021.
  • Greco, G. Angelotti, P. F. Caruso, A. Zanella, N. Stomeo, E. Costantini, A. Protti, A. Pesenti, G. Grasselli, and M. Cecconi. “Outcome prediction during an ICU surge using a purely data-driven approach: a supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak”, International Journal of Medical Informatics, vol. 164, p. 104807, 2022.
  • Tiwari, B. S. Bhati, F. Al‐Turjman, and B. Nagpal. “Pandemic coronavirus disease (Covid‐19): World effects analysis and prediction using machine‐learning techniques”, Expert Systems, vol. 39, no. 3, p. e12714, 2022.
  • R. Shahin, H. H. Alshammari, A. I. Taloba, and R. M. Abd El-Aziz, “Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus”. Computers and Electrical Engineering, vol. 101, p. 108055, 2022.
  • M. Kuo, P. C. Talley, and C. S. Chang, “The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis”, International Journal of Medical Informatics, vol. 164, p. 104791, 2022.
  • Moosazadeh, P. Ifaei, A. S. Tayerani Charmchi, S. Asadi, and C. Yoo, “A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the US counties”, Sustainable Cities and Society, vol. 83, p. 103990, 2022.
  • Mohebbi, M. Alavi, and M. Kargari, “Determining COVID-19 Severity with Fuzzy Inference System”. In 2022 27th International Computer Conference, Computer Society of Iran (CSICC), IEEE, 2022, pp. 1-5.
  • Anuja, and A. Bansal, “Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review”, Multimedia tools and applications, vol. 80, no. 13, pp. 19931-19946, 2021.
  • T. Huyut, “Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models”, IRBM, In Press, 2022.
  • Avuçlu, “A Novel Method Using Covid-19 Dataset and Machine Learning Algorithms FOR THE MOST ACCURATE DIAGNOSIS That can be Obtained in Medical Diagnosis”, Biomedical Signal Processing and Control, vol. 77, p. 103836, 2022.
  • Goel, R. Sindhgatta, S. Kalra, R. Goel, and P. Mutreja, “The effect of machine learning explanations on user trust for automated diagnosis of COVID-19”, Computers in Biology and Medicine, vol. 146, p. 105587, 2022.
  • de Fátima Cobre, M. Surek, D. P. Stremel, M. M. Fachi, H. H. L. Borba, F. S. Tonin, and R. Pontarolo, “Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning”, Computers in biology and medicine, vol. 146, p. 105659, 2022.
  • Shanbehzadeh, H. Kazemi-Arpanahi, and R. Nopour, “Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data”, Medical Journal of the Islamic Republic of Iran, vol. 35, p. 29, 2021.
  • Nopour, H. Kazemi-Arpanahi, M. Shanbehzadeh, and A. Azizifar, “Performance analysis of data mining algorithms for diagnosing COVID-19”, Journal of Education and Health Promotion, vol. 10, p. 405, 2021.
  • B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications”, Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
  • Zimmerman, and D. Kalra, “Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications”, Reviews in Cardiovascular Medicine, vol. 21, no. 3, pp. 345-352, 2020.
  • Tiwari, B. S. Bhati, F. Al‐Turjman, and B. Nagpal, “Pandemic coronavirus disease (Covid‐19): World effects analysis and prediction using machine‐learning techniques”, Expert Systems, vol. 39, no. 3, p. e12714, 2022.
  • S. Adamidi, K. Mitsis, and K. S. Nikita, “Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review”, Computational and Structural Biotechnology Journal, vol. 19, pp. 2833-2850, 2021.
  • Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai et al., “Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy”, Radiology, vol. 296, no. 2, E65-E71, 2020.
  • Bansal, G. Thakur, and D. Verma, “Detection of COVID-19 Using the CT Scan Image of Lungs”, In ISIC, vol. 21, pp. 25-27, 2021.
  • Karimiyan Abdar, S. M. Sadjadi, H. Soltanian-Zadeh, A. Bashirgonbadi, and M. Naghibi, “Automatic detection of coronavirus (COVID-19) from chest CT images using VGG16-based deep-learning”, In 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), IEEE, 2020, pp. 212-216.
  • Madaan, A. Roy, C. Gupta, P. Agrawal, A. Sharma, C. Bologa, and R. Prodan, “XCOVNet: chest X-ray image classification for COVID-19 early detection using convolutional neural networks”, New Generation Computing, vol. 39, no. 3, pp. 583-597, 2021.
  • Thakur, and A. Kumar, “X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)”, Biomedical Signal Processing and Control, vol. 69, p.102920, 2021.
  • Benmalek, J. Elmhamdi, and A. Jilbab, “Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis”, Biomedical Engineering Advances, vol. 1, p.100003, 2021.
  • Liang, H. Liu, Y. Gu, X. Guo, H. Li, L. Li, Z. Wu, M. Liu, and L. Tao, “Fast automated detection of COVID-19 from medical images using convolutional neural networks”, Communications Biology, vol. 4, no. 1, pp. 1-13, 2021.
  • Rahman, A. Khandakar, F. F. Abir, M. A. A. Faisal, M. S. Hossain, K. K. Podder, T. O. Abbas et al., “QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model”, Computers in Biology and Medicine, vol. 143, p. 105284, 2022.
  • C. Çubukçu, D. İ. Topcu, N. Bayraktar, M. Gülşen, N. Sarı, and A. H. Arslan, “Detection of COVID-19 by machine learning using routine laboratory tests”. American journal of clinical pathology, vol. 157, no. 5, pp. 758-766, 2022.
  • Ali, Y. Zhou, and M. Patterson, “Efficient analysis of covid-19 clinical data using machine learning models”, Medical & Biological Engineering & Computing, vol. 60, pp. 1815-1825.
  • F. de Moraes Batista, J. L. Miraglia, T. H. R. Donato, and A. D. P. Chiavegatto Filho, “COVID-19 diagnosis prediction in emergency care patients: a machine learning approach”. MedRxiv, 2020.
  • Brinati, Davide, Andrea Campagner, Davide Ferrari, Massimo Locatelli, Giuseppe Banfi, and Federico Cabitza. "Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study." Journal of medical systems 44, no. 8 (2020): 1-12.
  • J. Muhammad, M. Islam, S. S. Usman, and S. I. Ayon, “Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery”, SN Computer Science, vol. 1, no. 4, pp. 1-7, 2020.
  • A. de Freitas Barbosa, J. C. Gomes, M. A. de Santana, J. E. D. A. Albuquerque, R. G. de Souza, R. E. de Souza, and W. P. dos Santos, “Heg. IA: an intelligent system to support diagnosis of Covid-19 based on blood tests”, Research on Biomedical Engineering, vol. 38, no. 1, pp. 99-116, 2022.
  • A. Alves, G. Z. Castro, B. A. S. Oliveira, L. A. Ferreira, J A. Ramírez, R. Silva, and F. G. Guimarães, “Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs”, Computers in Biology and Medicine, vol. 132, p. 104335, 2021.
  • S. Yang, Y. Hou, L. V. Vasovic, P. A. Steel, A. Chadburn, S. E. Racine-Brzostek, P. Velu et al., “Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning”, Clinical chemistry, vol. 66, no. 11, pp. 1396-1404, 2020.
  • AlJame, I. Ahmad, A. Imtiaz, and A. Mohammed, “Ensemble learning model for diagnosing COVID-19 from routine blood tests”, Informatics in Medicine Unlocked, vol. 21, pp. 100449, 2020.
  • Banerjee, S. Ray, B. Vorselaars, J. Kitson, M. Mamalakis, S. Weeks, M. Baker, and L. S. Mackenzie, “Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population”. International immunopharmacology, vol. 86, p. 106705, 2020.
  • Cao, Z. Xu, J. Feng, C. Jin, X. Han, H. Wu, and H. Shi, “Longitudinal assessment of COVID-19 using a deep learning–based quantitative CT pipeline: illustration of two cases”, Radiology: Cardiothoracic Imaging, vol. 2, no. 2, p. e200082, 2020.
  • M. Rahmani, E. Yousefpoor, M. S. Yousefpoor, Z. Mehmood, A. Haider, M. Hosseinzadeh, and R. A. Naqvi, “Machine learning (ML) in medicine: review, applications, and challenges”, Mathematics, vol. 9, no. 22, p. 2970, 2021.
  • Uddin, A. Khan, M. E. Hossain, and M. A. Moni, ”Comparing different supervised machine learning algorithms for disease prediction”, BMC medical informatics and decision making, vol. 19, no. 1, pp. 1-16, 2019.
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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.
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International Journal of Web Research
Volume 5, Issue 1
Winter-Spring
June 2022
Pages 8-18

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