Machine Learning Based A Comparative Analysis for Predicting Intensive Care Unit Admission of COVID-19 Cases

Year 2023, , 1 - 9, 30.06.2023

Anıl Utku , Ümit Can

https://doi.org/10.46572/naturengs.1286352

Abstract

COVID-19 is a disease caused by the SARS-CoV-2 virus that emerged in December 2019 in Wuhan, China. This virus, which can be transmitted quickly, spread worldwide quickly, causing many people to be infected and even killed. The rapid course of the epidemic made managing medical resources difficult. Intensive care units play an important role in saving the lives of severely ill COVID-19 patients. In this study, a machine learning-based detection system was developed to predict the hospitalization of COVID-19 patients in intensive care units. Using a dataset of demographic characteristics and clinical findings of COVID-19 patients, DT, kNN, LR, MLP, NB, RF, and SVM were compared in practice using accuracy, recall, precision, and F-score. Experimental results showed that SVM has 0.964 accuracy, 0.957 precision, 0.971 recall, and 0.963 F-score.

Keywords

COVID-19, Machine Learning, Intensive Care Unit, SVM

References

• Baloch, S., Baloch, M. A., Zheng, T., & Pei, X. (2020). The coronavirus disease 2019 (COVID-19) pandemic. The Tohoku journal of experimental medicine, 250(4), 271-278.
• Jain, M. S., & Barhate, S. D. (2020). Corona viruses are a family of viruses that range from the common cold to MERS corona virus: A Review. Asian J Res Pharm Sci, 10(3), 204-10.
• Dhand, R., & Li, J. (2020). Coughs and sneezes: their role in transmission of respiratory viral infections, including SARS-CoV-2. American journal of respiratory and critical care medicine, 202(5), 651-659.
• Asadi, S., Bouvier, N., Wexler, A. S., & Ristenpart, W. D. (2020). The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles?. Aerosol Science and Technology, 54(6), 635-638.
• y Abuel-Reesh, J. (2017). A knowledge based system for diagnosing shortness of breath in infants and children. International Journal of Engineering and Information Systems (IJEAIS), 1(4), 102-115.
• Zaim, S., Chong, J. H., Sankaranarayanan, V., & Harky, A. (2020). COVID-19 and multiorgan response. Current problems in cardiology, 45(8), 100618.
• Arefi, M. F., & Poursadeqiyan, M. (2020). A review of studies on the COVID-19 epidemic crisis disease with a preventive approach. Work, 66(4), 717-729.
• Strielkowski, W., Zenchenko, S., Tarasova, A., & Radyukova, Y. (2022). Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications. Sustainability, 14(12), 7267.
• El-Sherif, D. M., Abouzid, M., Elzarif, M. T., Ahmed, A. A., Albakri, A., & Alshehri, M. M. (2022, February). Telehealth and Artificial Intelligence insights into healthcare during the COVID-19 pandemic. In Healthcare, 10(2), 385.
• Fusco, R., Grassi, R., Granata, V., Setola, S. V., Grassi, F., Cozzi, D., ... & Petrillo, A. (2021). Artificial intelligence and COVID-19 using chest CT scan and chest X-ray images: machine learning and deep learning approaches for diagnosis and treatment. Journal of Personalized Medicine, 11(10), 993.
• Rashid, M. T., & Wang, D. (2021). CovidSens: a vision on reliable social sensing for COVID-19. Artificial intelligence review, 54(1), 1-25.
• Jahmunah, V., Sudarshan, V. K., Oh, S. L., Gururajan, R., Gururajan, R., Zhou, X., ... & Acharya, U. R. (2021). Future IoT tools for COVID‐19 contact tracing and prediction: a review of the state‐of‐the‐science. International journal of imaging systems and technology, 31(2), 455-471.
• Andellini, M., De Santis, S., Nocchi, F., Bassanelli, E., Pecchia, L., & Ritrovato, M. (2020). Clinical needs and technical requirements for ventilators for COVID-19 treatment critical patients: an evidence-based comparison for adult and pediatric age. Health and Technology, 10(6), 1403-1411.
• Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339.
• Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals, 140, 110120.
• Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos, Solitons & Fractals, 139, 110017.
• Alazab, M., Awajan, A., Mesleh, A., Abraham, A., Jatana, V., & Alhyari, S. (2020). COVID-19 prediction and detection using deep learning. International Journal of Computer Information Systems and Industrial Management Applications, 12, 168-181.
• Cohen, J. P., Dao, L., Roth, K., Morrison, P., Bengio, Y., Abbasi, A. F., ... & Duong, T. Q. (2020). Predicting covid-19 pneumonia severity on chest x-ray with deep learning. Cureus, 12(7), e9448.
• Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., & Dong, Y. (2021). The ensemble deep learning model for novel COVID-19 on CT images. Applied soft computing, 98, 106885.
• Younis, M. C. (2021). Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction. Computerized Medical Imaging and Graphics, 90, 101921.
• Alassafi, M. O., Jarrah, M., & Alotaibi, R. (2022). Time series predicting of COVID-19 based on deep learning. Neurocomputing, 468, 335-344.
• Lorenzen, S. S., Nielsen, M., Jimenez-Solem, E., Petersen, T. S., Perner, A., Thorsen-Meyer, H. C., ... & Sillesen, M. (2021). Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark. Scientific reports, 11(1), 18959.
• Alabbad, D. A., Almuhaideb, A. M., Alsunaidi, S. J., Alqudaihi, K. S., Alamoudi, F. A., Alhobaishi, M. K., ... & Alshahrani, M. S. (2022). Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia. Informatics in Medicine Unlocked, 30, 100937.
• Covid-19 Clinical Data to assess diagnosis. https://www.kaggle.com/datasets/S%C3%ADrio Libanes/covid-19. (Accessed 3 March 2023)