Machine learning algorithms application in COVID-19 disease: a systematic literature review and future directions

Multidisciplinary Digital Publishing Institute (MDPI)
Switzerland

2022

REDICUC - Repositorio Universidad de La Costa

24 páginas; application/pdf

2079-9292

Electronics; 1. Ifeoluwapo, R.A.; Supriyanto, E.; Taheri, S. COVID-19 Death Risk Assessment in Iran using Artificial Neural Network. J. Phys. Conf. Ser. 2021, 1964, 062117. [CrossRef]; 2. Weyori, B.A.; Appiahene, P.; Kutiame, S.; Millham, R.; Adekoya, A.F.; Tettey, M. Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis Predicting Blocking Bugs View project Machine Learning and Big Financial Data View project Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis. IJACSA Int. J. Adv. Comput. Sci. Appl. 2022, 13, 2022. [CrossRef]; 3. Mohan, S.; Thirumalai, C.; Srivastava, G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 2019, 7, 81542–81554. [CrossRef]; 4. Zhong, X.; Ye, Y. Application of machine learning for predicting the spread of COVID-19. arXiv 2022, arXiv:2204.04364.; 5. Ellahham, S. Artificial intelligence in the diagnosis and management of COVID-19: A narrative review. J. Med. Artif. Intell. 2021. [CrossRef]; 6. Chamola, V.; Hassija, V.; Gupta, V.; Guizani, M. A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access 2020, 8, 90225–90265. [CrossRef]; 7. Manoj, M.; Srivastava, G.; Somayaji, S.R.K.; Gadekallu, T.R.; Maddikunta, P.K.R.; Bhattacharya, S. An Incentive Based Approach for COVID-19 planning using Blockchain Technology. In Proceedings of the IEEE Globecom Workshops, Taipei, Taiwan, 7–11 December 2020; pp. 1–6. [CrossRef]; 8. Buch, V.H.; Ahmed, I.; Maruthappu, M. Artificial intelligence in medicine: Current trends and future possibilities. Br. J. Gen. Pract. 2018, 68, 143–144. [CrossRef]; 9. Oh, S.H.; Lee, S.J.; Park, J. Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning. J. Pers. Med. 2022, 12, 87. [CrossRef]; 11. Shameer, K.; Johnson, K.W.; Glicksberg, B.S.; Dudley, J.T.; Sengupta, P.P. Machine learning in cardiovascular medicine: Are we there yet? Heart 2018, 104, 1156–1164. [CrossRef]; 12. Steuwer, B.; Eyal, N. SARS-CoV-2 human challenge studies. N. Engl. J. Med. 2021, 385, 1727–1728. [CrossRef]; 13. Ramos, C. COVID-19: La nueva enfermedad causada por un coronavirus. Salud Pública De Mex. 2020, 62, 225–227. [CrossRef]; 14. Rothan, H.A.; Byrareddy, S.N. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 2020, 109, 102433. [CrossRef]; 15. Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [CrossRef]; 16. Abas, A.H.; Marfuah, S.; Idroes, R.; Kusumawaty, D.; Park, M.N.; Siyadatpanah, A.; Alhumaydhi, F.; Mahmud, S.; Tallei, T.E.; Emran, T.B.; et al. Can the SARS-CoV-2 Omicron Variant Confer Natural Immunity against COVID-19? Molecules 2022, 27, 2221. [CrossRef]; 17. Mohapatra, R.K.; Kandi, V.; Tuli, H.S.; Chakraborty, C.; Dhama, K. The recombinant variants of SARS-CoV-2: Concerns continues amid COVID-19 pandemic. J. Med. Virol. 2022, 94, 3506. [CrossRef]; 18. Macedo, A.; Gonçalves, N.; Febra, C. COVID-19 fatality rates in hospitalized patients: Systematic review and meta-analysis. Ann. Epidemiol. 2021, 57, 14–21. [CrossRef]; 19. Chen, J.M. Novel statistics predict the COVID-19 pandemic could terminate in 2022. J. Med. Virol. 2022, 94, 2845–2848. [CrossRef]; 20. Gómez-Pavón, J.; González Del Castillo, J.; Martín-Delgado, M.; Martín-Sánchez, F.; Martínez-Sellés, M.; Molero García, J.; Moreno Guillén, S.; Rodríguez-Artalejo, F.; Ruiz-Galiana, J.; Cantón, R.; et al. COVID-19: Some unresolved issues. Rev. Esp. Quimioter. 2022, 35, 421–434. [CrossRef]; 21. Kommers, P.; Thanh, D.N.H.; Juwono, F.; Owan, V.J.; Akah, L.U.; Alawa, D.A. ICT Deployment for Teaching in the COVID-19 Era: A Quantitative Assessment of Availability and Challenges in Public Universities. Front. Educ. 2022, 7, 920932. [CrossRef]; 22. Dattner, I.; Huppert, A. Modern statistical tools for inference and prediction of infectious diseases using mathematical models. Stat. Methods Med. Res. 2018, 27, 1927–1929. [CrossRef]; 23. Rodríguez-Velásquez, J.O.; Prieto-Bohórquez, S.E.; Correa-Herrera, S.C.; Pérez-Díaz, C.E.; Soracipa-Muñoz, M.Y. Dinámica de la epidemia de malaria en Colombia: Predicción probabilística temporal. Rev. Salud Pública 2017, 19, 52–59. [CrossRef] [PubMed]; 24. Alanazi, S.A.; Kamruzzaman, M.M.; Alruwaili, M.; Alshammari, N.; Alqahtani, S.A.; Karime, A. Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care. J. Healthc. Eng. 2020, 2020, 8857346. [CrossRef] [PubMed]; 25. Lu, M.; Ishwaran, H. Cure and death play a role in understanding dynamics for COVID-19: Data-driven competing risk compartmental models, with and without vaccination. PLoS ONE 2021, 16, e0254397. [CrossRef] [PubMed]; 26. Haouari, M.; Mhiri, M. A particle swarm optimization approach for predicting the number of COVID-19 deaths. Sci. Rep. 2021, 11, 16587. [CrossRef] [PubMed]; 27. Bartoszko, J.; Dranitsaris, G.; Wilcox, M.E.; Del Sorbo, L.; Mehta, S.; Peer, M.; Parotto, M.; Bogoch, I.; Riazi, S. Development of a repeated-measures predictive model and clinical risk score for mortality in ventilated COVID-19 patients. Can. J. Anesth. 2022, 69, 343–352. [CrossRef] [PubMed]; 28. Hao, B.; Sotudian, S.; Wang, T.; Xu, T.; Hu, Y.; Gaitanidis, A.; Breen, K.; Velmahos, G.C.; Paschalidis, I.C. Early prediction of level-of-care requirements in patients with COVID-19. Elife 2020, 9, 1–23. [CrossRef]; 29. Williams, J.; Stebbing, J. COVID-19 and the risk to cancer patients in China. Int. J. Cancer 2021, 148, 265–266. [CrossRef]; 30. Boukhris, M.; Hillani, A.; Moroni, F.; Annabi, M.S.; Addad, F.; Harada, M.; Mansour, S.; Zhao, X.; Ybarra, L.F.; Abbate, A.; et al. Cardiovascular Implications of the COVID-19 Pandemic: A Global Perspective. Can. J. Cardiol. 2020, 36, 1068–1080. [CrossRef]; 31. Naudé, W. Artificial Intelligence versus COVID-19 in Developing Countries: Priorities and Trade-Offs; WIDER Background Note; UNU-WIDER: Helsinki, Sweden, 2020. [CrossRef]; 32. Unberath, M.; Ghobadi, K.; Levin, S.; Hinson, J.; Hager, G.D. Artificial Intelligence-based Clinical Decision Support for COVID19—Where Art Thou? Adv. Intell. Syst. 2020, 2, 2000104. [CrossRef]; 33. D Nguyen, D.C.; Ding, M.; Pathirana, P.N.; Seneviratne, A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID19)-Like Epidemics: A Survey. IEEE Access 2021, 9, 95730–95753. [CrossRef]; 34. Ulhaq, A.; Khan, A.; Gomes, D.; Paul, M. Computer Vision For COVID-19 Control: A Survey. arXiv 2020, arXiv:2004.09420. [CrossRef]; 35. Shaikh, F.; Andersen, M.; Sohail, M.; Mulero, F.; Awan, O.; Dupont-Roettger, D.; Kubassova, O.; Dehmeshki, J.; Bisdas, S. Current Landscape of Imaging and the Potential Role for Artificial Intelligence in the Management of COVID-19. Curr. Probl. Diagn. Radiol. 2021, 50, 430–435. [CrossRef]; 36. Alamo, T.; Reina, D.G.; Gata, P.M. Data-Driven Methods to Monitor, Model, Forecast and Control COVID-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory. arXiv 2020, arXiv:2006.01731. [CrossRef]; 37. Shinde, G.R.; Kalamkar, A.B.; Mahalle, P.N.; Dey, N.; Chaki, J.; Hassanien, A.E. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Comput. Sci. 2020, 1, 197. [CrossRef]; 38. Ahmad, A.; Garhwal, S.; Ray, S.K.; Kumar, G.; Malebary, S.J.; Barukab, O.M. The Number of Confirmed Cases of COVID-19 by using Machine Learning: Methods and Challenges. Arch. Comput. Methods Eng. 2020, 28, 2645–2653. [CrossRef]; 39. Kannan, S.; Subbaram, K.; Ali, S.; Kannan, H. The Role of Artificial Intelligence and Machine Learning Techniques: Race for COVID-19 Vaccine. Arch. Clin. Infect. Dis. 2020, 15, 103232. [CrossRef]; 40. Rahmatizadeh, S.; Valizadeh-Haghi, S.; Dabbagh, A. The role of artificial intelligence in management of critical COVID-19 patients. J. Cell. Mol. Anesth. 2020, 5, 16–22. [CrossRef]; 41. Bhattacharya, S.; Maddikunta, P.K.R.; Pham, Q.V.; Gadekallu, T.R.; Chowdhary, C.L.; Alazab, M.; Piran, M.J. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustain. Cities Soc. 2021, 65, 102589. [CrossRef]; 42. Henriquez, C.; Mardin, J.; Salcedo, D.; Pulgar-Emiliani, M.; Avendaño, I.; Angulo, L.; Pinedo, J. Predictive Model of Cardiovascular Diseases Implementing Artificial Neural Networks. In International Conference on Computer Information Systems and Industrial Management; Springer: Cham, Switzerland, 2022; pp. 231–242.; 43. Shah, S.K.; A McElfish, P. Cancer Screening Recommendations During the COVID-19 Pandemic: Scoping Review. JMIR Cancer 2022, 8, e34392. [CrossRef]; 44. Palazzuoli, A.; Lavie, C.J.; Severino, P.; Dastidar, A.; Sammut, E.; McCullough, P.A. Co-Management of COVID-19 and Heart Failure During the COVID-19 Pandemic: Lessons Learned. Rev. Cardiovasc. Med. 2022, 23, 218. [CrossRef]; 45. Bostanghadiri, N.; Jazi, F.M.; Razavi, S.; Fattorini, L.; Darban-Sarokhalil, D. Mycobacterium tuberculosis and SARS-CoV-2 Coinfections: A Review. Front. Microbiol. 2022, 12, 747827. [CrossRef] [PubMed]; 46. Al-Taie, A.; Arueyingho, O.; Khoshnaw, J.; Hafeez, A. Clinical outcomes of multidimensional association of type 2 diabetes mellitus, COVID-19 and sarcopenia: An algorithm and scoping systematic evaluation. Arch. Physiol. Biochem. 2022, 1–19. [CrossRef] [PubMed]; 47. Yusuf, E.; Seghers, L.; Hoek, R.A.S.; van den Akker, J.P.C.; Bode, L.G.M.; Rijnders, B.J.A. Aspergillus in critically ill COVID-19 patients: A scoping review. J. Clin. Med. 2021, 10, 2469. [CrossRef] [PubMed]; 48. Thatiparthi, A.; Martin, A.; Liu, J.; Egeberg, A.; Wu, J.J. Biologic Treatment Algorithms for Moderate-to-Severe Psoriasis with Comorbid Conditions and Special Populations: A Review. Am. J. Clin. Dermatol. 2021, 22, 425–442. [CrossRef] [PubMed]; 49. Mitaka, H.; Kuno, T.; Takagi, H.; Patrawalla, P. Incidence and mortality of COVID-19-associated pulmonary aspergillosis: A systematic review and meta-analysis. Mycoses 2021, 64, 993–1001. [CrossRef]; 50. Casalini, G.; Giacomelli, A.; Ridolfo, A.; Gervasoni, C.; Antinori, S. Invasive Fungal Infections Complicating COVID-19: A Narrative Review. J. Fungi 2021, 7, 921. [CrossRef]; 51. Khalsa, R.K.; Khashkhusha, A.; Zaidi, S.; Harky, A.; Bashir, M. Artificial intelligence and cardiac surgery during COVID-19 era. J. Card. Surg. 2021, 36, 1729–1733. [CrossRef]; 52. Douedi, S.; Mararenko, A.; Alshami, A.; Al-Azzawi, M.; Ajam, F.; Patel, S.; Douedi, H.; Calderon, D. COVID-19 induced bradyarrhythmia and relative bradycardia: An overview. J. Arrhythm. 2021, 37, 888–892. [CrossRef]; 53. Tamuzi, J.L.; Ayele, B.T.; Shumba, C.S.; Adetokunboh, O.O.; Uwimana-Nicol, J.; Haile, Z.T.; Inugu, J.; Nyasulu, P.S. Implications of COVID-19 in high burden countries for HIV/TB: A systematic review of evidence. BMC Infect. Dis. 2020, 20, 744. [CrossRef]; 54. Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.; PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Rev. Esp. Nutr. Hum. Diet. 2016, 20, 148–160. [CrossRef]; 55. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Syst. Rev. 2021, 10, 89. [CrossRef]; 56. Selcuk, A.A. A Guide for Systematic Reviews: PRISMA. Turk. Arch. Otorhinolaryngol. 2019, 57, 57–58. [CrossRef]; 57. Atlam, M.; Torkey, H.; El-Fishawy, N.; Salem, H. Coronavirus disease 2019 (COVID-19): Survival analysis using deep learning and Cox regression model. Pattern Anal. Appl. 2021, 24, 993–1005. [CrossRef]; 58. Khan, I.U.; Aslam, N.; Aljabri, M.; Aljameel, S.S.; Kamaleldin, M.M.A.; Alshamrani, F.M.; Chrouf, S.M.B. Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. Int. J. Environ. Res. Public Health 2021, 18, 6429. [CrossRef]; 59. Mohammad, R.M.A.; Aljabri, M.; Aboulnour, M.; Mirza, S.; Alshobaiki, A. Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. Appl. Comput. Intell. Soft Comput. 2022, 2022, 3783058. [CrossRef]; 60. Shafiekhani, S.; Rafiei, S.; Abdollahzade, S.; Souri, S.; Moomeni, Z. Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning. Pol. J. Med. Phys. Eng. 2022, 28, 19–29. [CrossRef]; 61. Hou, W.; Zhao, Z.; Chen, A.; Li, H.; Duong, T.Q. Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables. Int. J. Med. Sci. 2021, 18, 1739–1745. [CrossRef]; 62. Xu, W.; Sun, N.N.; Gao, H.N.; Chen, Z.Y.; Yang, Y.; Ju, B.; Tang, L. Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning. Sci. Rep. 2021, 11, 2933. [CrossRef]; 63. Ikemura, K.; Bellin, E.; Yagi, Y.; Billett, H.; Saada, M.; Simone, K.; Stahl, L.; Szymanski, J.; Goldstein, D.Y.; Gil, M.R. Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study. J. Med. Internet Res. 2021, 23, e23458. [CrossRef]; 64. Sankaranarayanan, S.; Balan, J.; Walsh, J.; Wu, Y.; Minnich, S. Piazza, A.; Osborne, C.; Oliver, G.; Lesko, J.; Bates, K.L.; et al. COVID-19 mortality prediction from deep learning in a large multistate electronic health record and laboratory information system data set: Algorithm development and validation. J. Med. Internet Res. 2021, 23, e30157. [CrossRef]; 65. Lima, T.P.F.; Sena, G.R.; Neves, C.S.; Vidal, S.A.; Lima, J.T.O.; Mello, M.J.G.; Silva, F.A. Death risk and the importance of clinical features in elderly people with COVID-19 using the random forest algorithm. Rev. Bras. Saúde Matern. Infant. 2021, 21, S445–S451. [CrossRef]; 66. Di Castelnuovo, A.; Bonaccio, M.; Costanzo, S.; Gialluisi, A.; Antinori, A.; Berselli, N.; Blandi, L.; Bruno, R.; Cauda, R.; Guaraldi, G.; et al. Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: Survival analysis and machine learning-based findings from the multicentre Italian CORIST Study. Nutr. Metab. Cardiovasc. Dis. 2020, 30, 1899–1913. [CrossRef] [PubMed]; 67. Guadiana-Alvarez, J.L.; Hussain, F.; Morales-Menendez, R.; Rojas-Flores, E.; García-Zendejas, A.; Escobar, C.A.; RamírezMendoza, R.A.; Wang, J. Prognosis patients with COVID-19 using deep learning. BMC Med. Inform. Decis. Mak. 2022, 22, 78. [CrossRef] [PubMed]; 68. Khadem, H.; Nemat, H.; Eissa, M.R.; Elliott, J.; Benaissa, M. COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework. Comput. Biol. Med. 2022, 144, 105361. [CrossRef] [PubMed]; 69. Meng, L.; Dong, D.; Li, L.; Niu, M.; Bai, Y.; Wang, M.; Qiu, X.; Zha, Y.; Tian, J. A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study. IEEE J. Biomed. Heal. Inform. 2020, 24, 3576–3584. [CrossRef]; 70. Nieto-Codesido, I.; Calvo-Alvarez, U.; Diego, C.; Hammouri, Z.; Mallah, N.; Ginzo-Villamayor, M.J.; Salgado, F.J.; Carreira, J.M.; Rábade, C.; Barbeito, G.; et al. Risk Factors of Mortality in Hospitalized Patients With COVID-19 Applying a Machine Learning Algorithm. Open Respir. Arch. 2022, 4, 100162. [CrossRef]; 71. Shanbehzadeh, M.; Valinejadi, A.; Afrah, R.; Kazemi-Arpanahi, H.; Orooji, A.; Kaffashian, M. Comparison of Machine-Learning Algorithms Efficiency to Build a Predictive Model for Mortality Risk in COVID-19 Hospitalized Patients. Koomesh 2022, 24, 128–138. Available online: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125205351&partnerID=40&md5=877e6 0c345b975a6d6c62b881ec38ca6 (accessed on 18 March 2022).; 72. Tezza, F.; Lorenzoni, G.; Azzolina, D.; Barbar, S.; Leone, L.; Gregori, D. Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques. J. Pers. Med. 2021, 11, 343. [CrossRef]; 73. Wang, J.M.; Liu, W.; Chen, X.; McRae, M.P.; McDevitt, J.T.; Fenyö, D. Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation. J. Med. Internet Res. 2021, 23, e29514. [CrossRef]; 74. Yu, L.; Halalau, A.; Dalal, B.; Abbas, A.E.; Ivascu, F.; Amin, M.; Nair, G.B. Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19. PLoS ONE 2021, 16, e0249285. [CrossRef]; 75. Amini, N.; Mahdavi, M.; Choubdar, H.; Abedini, A.; Shalbaf, A.; Lashgari, R. Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier. Comput. Methods Biomech. Biomed. Eng. 2022. [CrossRef]; 76. Becerra-Sánchez, A.; Rodarte-Rodríguez, A.; Escalante-García, N.I.; Olvera-González, J.E.; De la Rosa-Vargas, J.I.; Zepeda-Valles, G.; Velásquez-Martínez, E.D.J. Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques. Diagnostics 2022, 3, 1396. [CrossRef]; 77. Das, A.K.; Mishra, S.; Gopalan, S.S. Predicting COVID-19 community mortality risk using machine learning and development of an online prognostic tool. PeerJ 2020, 8, e10083. [CrossRef]; 78. Halasz, G.; Sperti, M.; Villani, M.; Michelucci, U.; Agostoni, P.; Biagi, A.; Rossi, L.; Botti, A.; Mari, C.; Maccarini, M.; et al. A machine learning approach for mortality prediction in COVID-19 pneumonia: Development and evaluation of the Piacenza score. J. Med. Internet Res. 2021, 23, e29058. [CrossRef]; 79. Kar, S.; Chawla, R.; Haranath, S.P.; Ramasubban, S.; Ramakrishnan, N.; Vaishya, R.; Sibal, A.; Reddy, S. Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID). Sci. Rep. 2021, 11, 12801. [CrossRef]; 80. Khozeimeh, F.; Sharifrazi, D.; Izadi, N.H.; Joloudari, J.H.; Shoeibi, A.; Alizadehsani, R.; Gorriz, J.M.; Hussain, S.; Sani, Z.A.; Moosaei, H.; et al. Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci. Rep. 2021, 11, 15343. [CrossRef]; 81. Parchure, P.; Joshi, H.; Dharmarajan, K.; Freeman, R.; Reich, D.L.; Mazumdar, M.; Timsina, P.; Kia, A. Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19. BMJ Support. Palliat. Care 2020, 12, e424–e431. [CrossRef]; 82. Rasmy, L.; Nigo, M.; Kannadath, B.S.; Xie, Z.; Mao, B.; Patel, K.; Zhou, Y.; Zhang, W.; Ross, A.; Xu, H.; et al. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: Model development and validation using electronic health record data. Lancet Digit. Health 2022, 4, e415–e425. [CrossRef]; 83. Ryan, L.; Lam, C.; Mataraso, S.; Allen, A.; Green-Saxena, A.; Pellegrini, E.; Hoffman, J.; Barton, C.; McCoy, A.; Das, R. Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study. Ann. Med. Surg. 2020, 59, 207–216. [CrossRef]; 84. Stachel, A.; Daniel, K.; Ding, D.; Francois, F.; Phillips, M.; Lighter, J. Development and validation of a machine learning model to predict mortality risk in patients with COVID-19. BMJ Health Care Inform. 2021, 28, e100235. [CrossRef]; 85. Yun, J.; Basak, M.; Han, M.-M. Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients. Comput. Mater. Contin. 2021, 69, 2827–2843. [CrossRef]; 86. Aggarwal, A.; Chakradar, M.; Bhatia, M.S.; Kumar, M.; Stephan, T.; Gupta, S.K.; Alsamhi, S.H.; Al-Dois, H. COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches. J. Healthc. Eng. 2022, 2022. [CrossRef] [PubMed]; 87. Ebrahimi, V.; Sharifi, M.; Mousavi-Roknabadi, R.S.; Sadegh, R.; Khademian, M.H.; Moghadami, M.; Dehbozorgi, A. Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: A machine-learning algorithm. BMC Public Health 2022, 22, 10. [CrossRef]; 88. Elghamrawy, S.M.; Hassanien, A.E.; Vasilakos, A.V. Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning. Int. J. Imaging Syst. Technol. 2021, 32, 614–628. [CrossRef]; 89. Ma, J.; Wang, Y.; Niu, X.; Jiang, S.; Liu, Z. A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression. Stoch. Environ. Res. Risk Assess. 2022, 36, 3109–3129. [CrossRef]; 90. Rockova, V.; McAlinn, K. Dynamic Variable Selection with Spike-and-Slab Process Priors. Bayesian Anal. 2021, 16, 233–269. [CrossRef]; 91. Medeiros, M.C.; Vasconcelos, G.; Veiga, Á.; Zilberman, E. Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods. J. Bus. Econ. Stat. 2019, 39, 98–119. [CrossRef]; 92. Mustafa, S.; Ali, A.; Salahuddin, H.; Chaudhry, M.U. Two-step Feature Selection for Predicting Mortality Risk in COVID-19 Patients. In Proceedings of the International Conference on Computing, Electronic and Electrical Engineering, ICE Cube, Quetta, Pakistan, 26–27 October 2021. [CrossRef]; 93. Why Accuracy Is Not A Good Metric For Imbalanced Data—Towards AI. Available online: https://towardsai.net/p/l/whyaccuracy-is-not-a-good-metric-for-imbalanced-data (accessed on 18 November 2022).; 94. Folleco, A.; Khoshgoftaar, T.M.; Napolitano, A. Comparison of four performance metrics for evaluating sampling techniques for low quality class-imbalanced data. In Proceedings of the 7th International Conference on Machine Learning and Applications, ICMLA 2008, San Diego, CA, USA, 11–13 December 2008; pp. 153–158. [CrossRef]; 95. Luque, A.; Carrasco, A.; Martín, A.; de las Heras, A. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognit. 2019, 91, 216–231. [CrossRef]; 96. Raeder, T.; Forman, G.; Chawla, N.V. Learning from Imbalanced Data: Evaluation Matters. Intell. Syst. Ref. Libr. 2012, 23, 315–331. [CrossRef]; 24; 23; 11; https://hdl.handle.net/11323/10139; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

10.3390/electronics11234015

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. ; Atribución 4.0 Internacional (CC BY 4.0) ; https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess ; http://purl.org/coar/access_right/c_abf2

edsbas.F74694D8