The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Abstract

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses.

Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation.

Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR.

Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

Keywords

deep and machine learning, COVID 19, prediction, detection, diagnosis, organizational management, hospital

References

1. Abdul Salam, M., Taha, S., & Ramadan, M. (2021). COVID-19 detection using federated machine learning. PLOS ONE, 16(6), e0252573.
   View in Google Scholar
2. Aktar, S., Ahamad, M. M., Rashed-Al-Mahfuz, M., Azad, A. K. M., Uddin, S., Kamal, A. H. M., Alyami, S. A., Lin, P., Islam, S. M. S., Quinn, J. M., Eapen, V., & Moni, M. A. (2021). Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: Statistical analysis and model development. JMIR Medical Informatics, 9(4), e25884.
   View in Google Scholar
3. Akter, S., Shamrat, F. M. J. M., Chakraborty, S., Karim, A., & Azam, S. (2021). COVID-19 detection using deep learning algorithm on chest X-ray images. Biology, 10, 1174.
   View in Google Scholar
4. Alassafi, M. O., Jarrah, M., & Alotaibi, R. (2022). Time series predicting of COVID-19 based on deep learning. Neurocomputing, 468, 335‒344.
   View in Google Scholar
5. Alballa, N., & Al-Turaiki, I. (2021). Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked, 24, 100564.
   View in Google Scholar
6. Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals, 140, 110120. https://doi. org/10.1016/j.chaos. 2020.110120.
   View in Google Scholar
7. Alali, Y., Harrou, F., & Sun, Y. (2022). A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Scientific Reports, 12, 2467.
   View in Google Scholar
8. Alotaibi, A., Shiblee, M., & Alshahrani, A. (2021). Prediction of severity of COVID-19-infected patients using machine learning techniques. Computers, 10, 31.
   View in Google Scholar
9. Al Shehri, W., Almalki, J., Mehmood, R., Alsaif, K., Alshahrani, S. M., Jannah, N., & Alangari, S. (2022). A novel COVID-19 detection technique using deep learning based approaches. Sustainability, 14, 12222.
   View in Google Scholar
10. Andrade, E. C. d., Pinheiro, P. R., Barros, A. L. B. d. P., Nunes, L. C., Pinheiro, L. I. C. C., Pinheiro, P. G. C. D., & Holanda Filho, R. (2022). Towards machine learning algorithms in predicting the clinical evolution of patients diagnosed with COVID-19. Applied Sciences, 12, 8939.
    View in Google Scholar
11. Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020). COVID-19 outbreak prediction with machine learning. Algorithms, 13, 249.
    View in Google Scholar
12. Arpaci, I., Huang, S., Al-Emran, M., Al-Kabi, M. N., & Peng, M. (2021). Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms. Multimedia Tools and Applications, 80, 11943–11957.
    View in Google Scholar
13. Arvind, V., Kim, J. S., Cho, B. H., Geng, E., & Cho, S. K. (2021). Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19. Journal of Critical Care, 62, 25–30.
    View in Google Scholar
14. Aslani, S., & Jacob, J. (2023). Utilisation of deep learning for COVID-19 diagnosis. Clinical Radiology, 78(2), 150–157.
    View in Google Scholar
15. Assaf, D., Gutman, Y., Neuman, Y., Segal, G., Amit, S., Gefen-Halevi, S., Shilo, N., Epstein, A., Mor-Cohen, R., Biber, A., Rahav, G., Levy, I., & Tirosh, A. (2020). Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Internal and Emergency Medicine, 15, 1435–1443.
    View in Google Scholar
16. Babukarthik, R. G., Adiga, V. A. K., Sambasivam, G., Chandramohan, D., & Amudhavel, J. (2020). Prediction of COVID-19 using Genetic Deep Learning Convolutional Neural Network (GDCNN). IEEE Access, 8, 177647–177666.
    View in Google Scholar
17. Burdick, H., Lam, C., Mataraso, S., Siefkas, A., Braden, G., Phillip Dellinger, R., McCoy, A., Vincent, J.-L., Green-Saxena, A., Barnes, G., Hoffman, J., Calvert, J., Pellegrini, E., & Das, R. (2020). Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Computers in Biology and Medicine, 124, 103949.
    View in Google Scholar
18. Cobre, A. F., Stremel, D. P., Noleto, G. R., Fachi, M. M., Surek, M., Wiens, A., Tonin, F. S., & Pontarolo, R. (2021). Diagnosis and prediction of COVID-19 severity: Can biochemical tests and machine learning be used as prognostic indicators? Computers in Biology and Medicine, 134, 104531.
    View in Google Scholar
19. Das, A. K., Mishra, S., & Saraswathy Gopalan, S. (2020). Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool. PeerJ, 8, e10083.
    View in Google Scholar
20. Desai, S. B., Pareek, A., & Lungren, M. P. (2020). Deep learning and its role in COVID-19 medical imaging. Intelligence-Based Medicine, 3–4, 100013.
    View in Google Scholar
21. Devaraj, J., Elavarasan, R. M., Pugazhendhi, R., Shafiullah, G. M., Ganesan, S., Jeysree, A. K., Khan, I. A., & Hossain, E. (2021). Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant? Results in Physics, 21, 103817.
    View in Google Scholar
22. Ebinger, J., Wells, M., Ouyang, D., Davis, T., Kaufman, N., Cheng, S., & Chugh, S. (2021). A machine learning algorithm predicts duration of hospitalization in COVID-19 patients. Intelligence-Based Medicine, 5, 100035.
    View in Google Scholar
23. Elham, J., Amirhossein, A., Nader, T., Alireza, Z., Soroush, S., Hadi, E., Jamaldini, S. H., Daaee, A., Babajani, A., Kashi, M. A. S., Jamshidi, M., Rahi, S. J., & Mansouri, N. (2022). Using machine learning to predict mortality for COVID-19 patients on day 0 in the ICU. Frontiers in Digital Health, 3, 681608.
    View in Google Scholar
24. Elhazmi, A., Al-Omari, A., Sallam, H., Mufti, H. N., Rabie, A. A., Alshahrani, M., Mady, A., Alghamdi, A., Altalaq, A., Azzam, M. H., Sindi, A., Kharaba, A., Al-Aseri, Z. A., Almekhlafi, G. A., Tashkandi, W., Alajmi, S. A., Faqihi, F., Alharthy, A., Al-Tawfiq, J. A., Melibari, R. G., Al-Hazzani, W., & Arabi, Y. M. (2022). Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU. Journal of Infection and Public Health, 15(7), 826–834.
    View in Google Scholar
25. Fang, C., Bai, S., Chen, Q., Zhou, Y., Xia, L., Qin, L., Gong, S., Xie, X., Zhou, C., Tu, D., Zhang, C., Liu, X., Chen, W., Bai, X., & Torr, P. H. S. (2021). Deep learning for predicting COVID-19 malignant progression. Medical Image Analysis, 72, 102096.
    View in Google Scholar
26. Fernandes, F. T., de Oliveira, T. A., Teixeira, C. E., de Moraes Batista, A. F., Costa, G. D., & Filho, A. D. P. C. (2021). A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil. Scientific Reports, 11, 3343.
    View in Google Scholar
27. Goodman-Meza, D., Rudas, A., Chiang, J. N., Adamson, P. C., Ebinger, J., Sun, N., Botting, P., Fulcher, J. A., Saab, F. G., Brook, R., Eskin, E., An, U., Kordi, M., Jew, B., Balliu, B., Chen, Z., Hill, B. L., Rahmani, E., Halperin, E., & Manuel, V. (2020). A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity. PLoS ONE, 15(9), e0239474.
    View in Google Scholar
28. Gothai, E., Thamilselvan, R., Rajalaxmi, R. R., Sadana, R. M., Ragavi, A., & Sakthivel, R. (2023). Prediction of COVID-19 growth and trend using machine learning approach. Materials Today: Proceedings, 81(2), 597–601.
    View in Google Scholar
29. Halasz, G., Sperti, M., Villani, M., Michelucci, U., Agostoni, P., Biagi, A., Rossi, L., Botti, A., Mari, C., Maccarini, M., Pura, F., Roveda, L., Nardecchia, A., Mottola, E., Nolli, M., Salvioni, E., Mapelli, M., Deriu, M. A., Piga, D., & Piepoli M. (2021). A machine learning approach for mortality prediction in COVID-19 pneumonia: Development and evaluation of the Piacenza Score. Journal of Medical Internet Research, 23(5), e29058.
    View in Google Scholar
30. Ikemura, K., Bellin, E., Yagi, Y., Billett, H., Saada, M., Simone, K., Stahl, L., Szymanski, J., Goldstein, D. Y., & Reyes Gil, M. (2021). Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study. Journal of Medical Internet Research, 23(2), e23458.
    View in Google Scholar
31. Iwendi, C., Huescas, C. G. Y., Chakraborty, C., & Mohan, S. (2022). COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients. Journal of Experimental & Theoretical Artificial Intelligence.
    View in Google Scholar
32. Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., La Spada, L., Mirmozafari, M., Dehghani, M., Sabet, A., Roshani, S., Roshani, S., Bayat-Makou, N., Mohamadzade, B., Malek, Z., Jamshidi, A., Kiani, S., Hashemi-Dezaki, H., & Mohyuddin, W. (2020). Artificial intelligence and COVID-19: Deep learning approaches for diagnosis and treatment. IEEE Access, 8, 109581–109595.
    View in Google Scholar
33. Kallel, A., Rekik, M., & Khemakhem, M. (2022). Hybrid-based framework for COVID-19 prediction via federated machine learning models. Journal of Supercomputing, 78, 7078–7105.
    View in Google Scholar
34. Kar, S., Chawla, R., Haranath, S. P., Ramasubban, S., Ramakrishnan, N., Vaishya, R., Sibal, A., & Reddy, S. (2021). Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID). Scientific Reports, 11, 12801.
    View in Google Scholar
35. Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2023). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Applied Nanoscience, 13, 2013–2025.
    View in Google Scholar
36. Laatifi, M., Douzi, S., Bouklouz, A., Ezzine, H., Jaafari, J., Zaid, Y., El Ouahidi, B., & Naciri, M. (2022). Machine learning approaches in Covid-19 severity risk prediction in Morocco. Journal of Big Data, 9, 5.
    View in Google Scholar
37. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, 110059.
    View in Google Scholar
38. Li, X., Ge, P., Zhu, J., Li, H., Graham, J., Singer, A., Richman, P. S., & Duong, T. Q. (2020). Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables. PeerJ, 8, e10337.
    View in Google Scholar
39. Majhi, R., Thangeda, R., Sugasi, R. P., & Kumar N. (2021). Analysis and prediction of COVID-19 trajectory: A machine learning approach. Journal of Public Affairs, 21(4), e2537.
    View in Google Scholar
40. Malki, Z., Atlam, E. S., Hassanien, A. E., Dagnew, G., Elhosseini, M. A., & Gad, I. (2020). Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons & Fractals, 138, 110137.
    View in Google Scholar
41. Malki, Z., Atlam, E. S., Ewis, A., Dagnew, G., Ghoneim, O. A., Mohamed, A. A., Abdel-Daim, M. M., & Gad, I. (2021). The COVID-19 pandemic: Prediction study based on machine learning models. Environmental Science and Pollution Research, 28, 40496–40506.
    View in Google Scholar
42. Meraihi, Y., Gabis, A. B., Mirjalili, S., Ramdane-Cherif, A., & Alsaadi, F. E. (2022). Machine learning-based research for COVID-19 detection, diagnosis, and prediction: A survey. SN Computer Science, 3, 286.
    View in Google Scholar
43. Mohan, S., John, A., Abugabah, A., Adimoolam, M., Kumar Singh, S., Kashif Bashir, A., & Sanzogni, L. (2022). An approach to forecast impact of Covid-19 using supervised machine learning model. Software: Practice and Experience, 52(4), 824–840.
    View in Google Scholar
44. Moulaei, K., Shanbehzadeh, M., Mohammadi-Taghiabad, Z., & Kazemi-Arpanahi, H. (2022). Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Medical Informatics and Decision Making, 22, 2.
    View in Google Scholar
45. Muhammad, L. J., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021). Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN Computer Science, 2, 11.
    View in Google Scholar
46. Naeem, M., Yu, J., Aamir, M., Khan, S. A., Adeleye, O., & Khan, Z. (2021). Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak. PeerJ Computer Science, 7, e746.
    View in Google Scholar
47. Nassif, A. B., Shahin, I., Bader, M., Hassan, A., & Werghi, N. (2022). COVID-19 detection systems using deep-learning algorithms based on speech and image data. Mathematics, 10, 564.
    View in Google Scholar
48. Nemati, M., Ansary, J., & Nemati, N. (2020). Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns, 1(5), 100074.
    View in Google Scholar
49. Ning, W., Lei, S., Yang, J., Cao, Y., Jiang, P., Yang, Q., Zhang, J., Wang, X., Chen, F., Geng, Z., Xiong, L., Zhou, H., Guo, Y., Zeng, Y., Shi, H., Wang, L., Xue, Y., & Wang, Z. (2020). Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning. Nature Biomedical Engineering, 4, 1197–1207.
    View in Google Scholar
50. Pan, P., Li, Y., Xiao, Y., Han, B., Su, L., Su, M., Li, Y., Zhang, S., Jiang, D., Chen, X., Zhou, F., Ma, L., Bao, P., Xie, L. (2020). Prognostic assessment of COVID-19 in the intensive care unit by machine learning methods: Model development and validation. Journal of Medical Internet Research, 22(11), e23128.
    View in Google Scholar
51. Patel, D., Kher, V., Desai, B., Lei, X., Cen, S., Nanda, N., Gholamrezanezhad, A., Duddalwar, V., Varghese, B., & Oberai, A. A. (2021). Machine learning based predictors for COVID-19 disease severity. Scientific Reports, 11, 4673.
    View in Google Scholar
52. Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R. (2020). COVID-19 pandemic prediction for Hungary; A hybrid machine learning approach. Mathematics, 8, 890.
    View in Google Scholar
53. Pourhomayoun, M., & Shakibi, M. (2021). Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health, 20, 100178.
    View in Google Scholar
54. Quiroz-Juárez, M. A., Torres-Gómez, A., Hoyo-Ulloa, I., León-Montiel, R. D. J., & U’Ren, A. B. (2021). Identification of high-risk COVID-19 patients using machine learning. PLOS ONE, 16(9), e0257234.
    View in Google Scholar
55. Rahimi, I., Chen, F., & Gandomi, A. H. (2023). A review on COVID-19 forecasting models. Neural Computing and Applications, 35, 23671–23681.
    View in Google Scholar
56. Rahimi, I., Gandomi, A. H., Asteris, P. G., & Chen, F. (2021). Analysis and prediction of COVID-19 using SIR, SEIQR, and machine learning models: Australia, Italy, and UK cases. Information, 12, 109.
    View in Google Scholar
57. Rahman, M. M., Islam, M. M., Manik, M. M. H., Islam, M. R., & Al-Rakhami, M. S. (2021). Machine learning approaches for tackling novel coronavirus (COVID-19) pandemic. SN Computer Science, 2, 384.
    View in Google Scholar
58. Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B.-W., Aslam, A., & Choi, G. S. (2020). COVID-19 future forecasting using supervised machine learning models. IEEE Access, 8, 101489–101499.
    View in Google Scholar
59. Saba, T., Abunadi, I., Shahzad, M. N., & Khan, A. R. (2021). Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microscopy Research and Technique, 84(7), 1462–1474. https://doi.org/ 10.1002/jemt.23702.
    View in Google Scholar
60. Satu, M. S., Howlader, K. C., Mahmud, M., Kaiser, M. S., Shariful Islam, S. M., Quinn, J. M. W., Alyami, S. A., & Moni, M. A. (2021). Short-term prediction of COVID-19 cases using machine learning models. Applied Sciences, 11, 4266.
    View in Google Scholar
61. Sayed, S. A.-F., Elkorany, A. M., & Sayed Mohammad, S. (2021). Applying different machine learning techniques for prediction of COVID-19 severity. IEEE Access, 9, 135697–135707.
    View in Google Scholar
62. Shahin, O. R., Alshammari, H. H., Taloba, A. I., & El-Aziz, R. M. A. (2022). Machine learning approach for autonomous detection and classification of COVID-19 virus. Computers and Electrical Engineering, 101, 108055.
    View in Google Scholar
63. Solayman, S., Aumi, S. A., Mery, C. S., Mubassir, M., & Khan, R. (2023). Automatic COVID-19 prediction using explainable machine learning techniques. International Journal of Cognitive Computing in Engineering, 4, 36–46.
    View in Google Scholar
64. Su, X., Sun, Y., Liu, H., Lang, Q., Zhang, Y., Zhang, J., Wang, C., & Chen, Y. (2023). An innovative ensemble model based on deep learning for predicting COVID-19 infection. Scientific Reports, 13, 12322.
    View in Google Scholar
65. Subudhi, S., Verma, A., Patel, A. B., Hardin, C. C., Khandekar, M. J., Lee, H., McEvoy, D., Stylianopoulos, T., Munn, L. L., Dutta, S., & Jain, R. K. (2021). Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. npj Digital Medicine, 4, 87.
    View in Google Scholar
66. Tiwari, S., Chanak, P., & Singh, S. K. (2022). A review of the machine learning algorithms for Covid-19 case analysis. IEEE Transactions on Artificial Intelligence, 4(1), 44–59.
    View in Google Scholar
67. Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11, 100222.
    View in Google Scholar
68. Ustebay, S., Sarmis, A., Kaya, G. K., & Sujan, M. (2023). A comparison of machine learning algorithms in predicting COVID-19 prognostics. Internal and Emergency Medicine, 18, 229–239.
    View in Google Scholar
69. Vepa, A., Saleem, A., Rakhshan, K., Daneshkhah, A., Sedighi, T., Shohaimi, S., Omar, A., Salari, N., Chatrabgoun, O., Dharmaraj, D., Sami, J., Parekh, S., Ibrahim, M., Raza, M., Kapila, P., & Chakrabarti, P. (2021). Using machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatients. International Journal of Environmental Research and Public Health, 18, 6228.
    View in Google Scholar
70. Verma, H., Mandal, S., & Gupta, A. (2022). Temporal deep learning architecture for prediction of COVID-19 cases in India. Expert Systems with Applications, 195, 116611.
    View in Google Scholar
71. Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J.-H., & Hsieh, J.-G. (2021). COVID-19 Prediction applyin supervised machine learning algorithms with comparative analysis using WEKA. Algorithms, 14, 201.
    View in Google Scholar
72. Xu, W., Sun, N.-N., Gao, H.-N., Chen, Z.-Y., Yang, Y., Ju, B., & Tang, L.-L. (2021). Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning. Scientific Reports, 11, 2933.
    View in Google Scholar
73. Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210.
    View in Google Scholar
74. Yu, L., Halalau, A., Dalal, B., Abbas, A. E., Ivascu, F., Amin, M., & Nair, G. B. (2021). Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19. PLOS ONE, 16(4), e0249285.
    View in Google Scholar
75. Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals, 140, 110121.
    View in Google Scholar
76. Zhang, X., Lu, S., Wang, S.-H., Yu, X., Wang, S.-J., Yao, L., Pan, Y., & Zhang, Y.-D. (2022). Diagnosis of COVID-19 pneumonia via a novel deep learning architecture. Journal of Computer Science and Technology, 37, 330–343.
    View in Google Scholar
77. Zivkovic, M., Bacanin, N., Venkatachalam, K., Nayyar, A., Djordjevic, A., Strumberger, I., & Al-Turjman, F. (2021). COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustainable Cities and Society, 66, 102669.
    View in Google Scholar
78. Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj Digital Medicine, 4, 3.
    View in Google Scholar