{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:39:37Z","timestamp":1763105977050,"version":"3.41.2"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.<\/jats:p>","DOI":"10.3389\/frai.2023.1327355","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T05:05:25Z","timestamp":1704949525000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases"],"prefix":"10.3389","volume":"6","author":[{"given":"Areej","family":"Alhhazmi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Alferidi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahya A.","family":"Almutawif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hatim","family":"Makhdoom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hibah M.","family":"Albasri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben Slama","family":"Sami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"105244","DOI":"10.1016\/j.compbiomed.2022.105244","article-title":"COVID-19 diagnosis using state-of-the-art CNN Architecture features and Bayesian optimization","volume":"142","author":"Aslan","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.cie.2010.11.016","article-title":"An integrated artificial neural network fuzzy c-means-normalization algorithm for performance assessment of decision-making units: the cases of auto industry and Power Plant","volume":"60","author":"Azadeh","year":"2011","journal-title":"Comput. Ind. Eng."},{"key":"B3","doi-asserted-by":"publisher","first-page":"104107","DOI":"10.1016\/j.jappgeo.2020.104107","article-title":"MPs realization selection with an innovative LSTM tool","volume":"179","author":"Azamifard","year":"2020","journal-title":"J. Appl. Geophys."},{"key":"B4","doi-asserted-by":"publisher","first-page":"109181","DOI":"10.1016\/j.asoc.2022.109181","article-title":"Covid-19 ICU demand forecasting: a two-stage Prophet-LSTM approach","volume":"125","author":"Borges","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"B5","first-page":"49","article-title":"\u201cEmbedding formative assessment: a peek inside the black box,\u201d","volume-title":"Formative Assessment in United States Classrooms","author":"Box","year":"2018"},{"key":"B6","doi-asserted-by":"publisher","first-page":"105363","DOI":"10.1016\/j.engappai.2022.105363","article-title":"Interval type-3 fuzzy fractal approach in sound speaker quality control evaluation","volume":"116","author":"Castillo","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"B7","doi-asserted-by":"publisher","first-page":"114433","DOI":"10.1016\/j.jviromet.2021.114433","article-title":"Time series analysis and predicting COVID-19 affected patients by Arima model using machine learning","volume":"301","author":"Chyon","year":"2022","journal-title":"J. Virol. Methods"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e3042025","DOI":"10.17061\/phrp3042025","article-title":"The Australian Health System response to COVID-19 from a resilient health care perspective: what have we learned?","volume":"30","author":"Clay-Wililams","year":"2020","journal-title":"Public Health Res. Pract."},{"key":"B9","doi-asserted-by":"publisher","first-page":"103791","DOI":"10.1016\/j.jbi.2021.103791","article-title":"Comparative study of machine learning methods for COVID-19 transmission forecasting","volume":"118","author":"Dairi","year":"2021","journal-title":"J. Biomed. Inform."},{"key":"B10","doi-asserted-by":"publisher","first-page":"e210097","DOI":"10.1148\/ryai.2021210097","article-title":"Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification","volume":"3","author":"Eche","year":"2021","journal-title":"Radiol. Artif. Intelli."},{"key":"B11","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1177\/09514848211010264","article-title":"Managing healthcare services: are professionals ready to play the role of manager?","volume":"35","author":"Fanelli","year":"2021","journal-title":"Health Serv. Manag. Res."},{"key":"B12","doi-asserted-by":"publisher","first-page":"105915","DOI":"10.1016\/j.compbiomed.2022.105915","article-title":"A novel approach for COVID-19 infection forecasting based on multi-source deep transfer learning","volume":"149","author":"Garg","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"e00429","DOI":"10.1016\/j.susmat.2022.e00429","article-title":"Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model","volume":"32","author":"Ghimire","year":"2022","journal-title":"Sustain. Mater. Technol."},{"key":"B14","first-page":"403","article-title":"\u201cEarly prediction of COVID-19 outcome: contrasting clinical scores and computational intelligence methods,\u201d","volume-title":"Understanding COVID-19: The Role of Computational Intelligence","author":"Greco","year":"2021"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e09535","DOI":"10.1016\/j.heliyon.2022.e09535","article-title":"Environmental benefits of blue ecosystem services and residents' willingness to pay in Khulna City, Bangladesh","volume":"8","author":"Haque","year":"2022","journal-title":"Heliyon"},{"key":"B16","doi-asserted-by":"publisher","first-page":"100390","DOI":"10.1016\/j.sste.2020.100390","article-title":"A district-level susceptibility and vulnerability assessment of the COVID-19 pandemic's footprint in India","volume":"36","author":"Imdad","year":"2021","journal-title":"Spat. Spatiotemporal. Epidemiol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"109536","DOI":"10.1016\/j.buildenv.2022.109536","article-title":"Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction","volume":"224","author":"Jiang","year":"2022","journal-title":"Build. Environ."},{"key":"B18","first-page":"449","article-title":"\u201cCOVID-19, nutrition, immunity, and diet,\u201d","volume-title":"Delineating Health and Health System: Mechanistic Insights into Covid","author":"Khetarpaul","year":"2021"},{"key":"B19","doi-asserted-by":"publisher","first-page":"e11185","DOI":"10.1016\/j.heliyon.2022.e11185","article-title":"Predictive models for COVID-19 detection using routine blood tests and machine learning","volume":"8","author":"Kistenev","year":"2022","journal-title":"Heliyon"},{"key":"B20","doi-asserted-by":"publisher","first-page":"60","DOI":"10.28978\/nesciences.868087","article-title":"Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks","volume":"6","author":"Kutlu","year":"2021","journal-title":"Nat. Eng. Sci."},{"key":"B21","doi-asserted-by":"publisher","first-page":"117623","DOI":"10.1016\/j.apenergy.2021.117623","article-title":"Day-Ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model","volume":"303","author":"Li","year":"2021","journal-title":"Appl. Energy"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.5121\/csit.2022.121215","article-title":"An online graphical user interface application to remove barriers in the process of learning neural networks and deep learning concepts using Tensorflow","author":"Li","year":"2022","journal-title":"Artif. Intell. Mach. Learn"},{"key":"B23","doi-asserted-by":"publisher","first-page":"210","DOI":"10.3390\/analytics1020014","article-title":"Using internet search data to forecast COVID-19 trends: a systematic review","volume":"1","author":"Ma","year":"2022","journal-title":"Analytics"},{"key":"B24","doi-asserted-by":"publisher","first-page":"101068","DOI":"10.1016\/j.disamonth.2020.101068","article-title":"COVID-19 medical management including World Health Organization (WHO) suggested management strategies","volume":"66","author":"McFee","year":"2020","journal-title":"Disease-a-Month"},{"key":"B25","doi-asserted-by":"publisher","first-page":"102394","DOI":"10.1016\/j.artmed.2022.102394","article-title":"Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model","volume":"132","author":"Md Saleh","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"B26","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1201\/9781003175865-5","volume-title":"Artificial Intelligence Theory, Models, and Applications","author":"Nair","year":"2021"},{"key":"B27","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2021.10.043","article-title":"Digital twins based on bidirectional LSTM and gan for modelling the COVID-19 pandemic","volume":"470","author":"Quilodr\u00e1n-Casas","year":"2022","journal-title":"Neurocomputing"},{"key":"B28","doi-asserted-by":"publisher","first-page":"214","DOI":"10.26524\/royal.37.21","article-title":"\u201cManagement strategies of COVID\u221219,\u201d","author":"Reshi","year":"2020","journal-title":"COVID-19 Pandemic Update"},{"key":"B29","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.ifacol.2022.09.425","article-title":"Demand forecasting of a multinational retail company using Deep Learning Frameworks","volume":"55","author":"Saha","year":"2022","journal-title":"IFAC-Papers OnLine"},{"key":"B30","doi-asserted-by":"publisher","first-page":"134350","DOI":"10.1016\/j.jclepro.2022.134350","article-title":"Unscramble social media power for waste management: a multilayer deep learning approach","volume":"377","author":"Shahidzadeh","year":"2022","journal-title":"J. Clean. Prod."},{"key":"B31","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.jbusres.2020.09.068","article-title":"Augmenting organizational decision-making with deep learning algorithms: principles, promises, and challenges","volume":"123","author":"Shrestha","year":"2021","journal-title":"J. Bus. Res."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.3390\/ijerph19031339","article-title":"Common demand vs. limited supply\u2014how to serve the global fight against COVID-19 through proper supply of COVID-19 vaccines","volume":"19","author":"Su","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"B33","doi-asserted-by":"publisher","first-page":"299","DOI":"10.23940\/ijpe.21.03.p5.299306","article-title":"LSTM and RNN to predict COVID cases: lethality's and tests in GCC nations and India","volume":"17","author":"Sulthana","year":"2021","journal-title":"Int. J. Performability Eng."},{"key":"B34","doi-asserted-by":"publisher","first-page":"6001876","DOI":"10.1155\/2022\/6001876","article-title":"COVID-19 pandemic data modeling in Pakistan using time-series sir","volume":"2022","author":"Taimoor","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"B35","doi-asserted-by":"publisher","first-page":"100085","DOI":"10.1016\/j.array.2021.100085","article-title":"Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM","volume":"11","author":"Vadyala","year":"2021","journal-title":"Array"},{"key":"B36","doi-asserted-by":"publisher","first-page":"100455","DOI":"10.1016\/j.sste.2021.100455","article-title":"Robust trend estimation for covid-19 in Brazil","volume":"39","author":"Valente","year":"2021","journal-title":"Spat. Spatiotemporal. Epidemiol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"100587","DOI":"10.23880\/EIJ-16000227","article-title":"Synchronization of epidemic curves of COVID-19 among nearby countries","volume":"6","author":"Yoshikura","year":"2022","journal-title":"Epidemiol. Int. J."},{"key":"B38","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3390\/membranes11020100","article-title":"Prediction of the long-term effect of iron on methane yield in an anaerobic membrane bioreactor using Bayesian network meta-analysis","volume":"11","author":"Yu","year":"2021","journal-title":"Membranes"},{"key":"B39","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.ins.2022.10.078","article-title":"Advanced predictive control for GRU and LSTM networks","volume":"616","author":"Zarzycki","year":"2022","journal-title":"Inf. Sci."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1327355\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T14:52:28Z","timestamp":1707144748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1327355\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,11]]},"references-count":39,"alternative-id":["10.3389\/frai.2023.1327355"],"URL":"https:\/\/doi.org\/10.3389\/frai.2023.1327355","relation":{},"ISSN":["2624-8212"],"issn-type":[{"type":"electronic","value":"2624-8212"}],"subject":[],"published":{"date-parts":[[2024,1,11]]},"article-number":"1327355"}}