{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:57:42Z","timestamp":1780624662853,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior, Brasil (CAPES)","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior, Brasil (CAPES)","award":["06\/2017"],"award-info":[{"award-number":["06\/2017"]}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Santa Catarina (FAPESC)","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Santa Catarina (FAPESC)","award":["06\/2017"],"award-info":[{"award-number":["06\/2017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients\u2019 behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient\u2019s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient\u2019s scheduling.<\/jats:p>","DOI":"10.3390\/info13110507","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T04:40:36Z","timestamp":1666586436000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5842-8451","authenticated-orcid":false,"given":"Luiz Henrique Am\u00e9rico","family":"Salazar","sequence":"first","affiliation":[{"name":"Master Program in Applied Computer Science, School of Sea, Science and Technology, University of Vale do Itaja\u00ed, Itaja\u00ed 88302-901, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1896-0520","authenticated-orcid":false,"given":"Wemerson Delcio","family":"Parreira","sequence":"additional","affiliation":[{"name":"Master Program in Applied Computer Science, School of Sea, Science and Technology, University of Vale do Itaja\u00ed, Itaja\u00ed 88302-901, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2986-5353","authenticated-orcid":false,"given":"Anita Maria da Rocha","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Master Program in Applied Computer Science, School of Sea, Science and Technology, University of Vale do Itaja\u00ed, Itaja\u00ed 88302-901, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-9271","authenticated-orcid":false,"given":"Valderi Reis Quietinho","family":"Leithardt","sequence":"additional","affiliation":[{"name":"VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Polit\u00e9cnico de Portalegre, 7300-555 Portalegre, Portugal"},{"name":"COPELABS, Universidade Lus\u00f3fona de Humanidades e Tecnologias, 1749-024 Lisboa, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.cali.2017.01.004","article-title":"An\u00e1lisis del coste econ\u00f3mico del absentismo de pacientes en consultas externas","volume":"32","author":"Mesa","year":"2017","journal-title":"Rev. De Calid. Asist."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Batool, T., Abuelnoor, M., El Boutari, O., Aloul, F., and Sagahyroon, A. (2021, January 27\u201328). Predicting hospital no-shows using machine learning. Proceedings of the 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Bali, Indonesia.","DOI":"10.1109\/IoTaIS50849.2021.9359692"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/20479700.2019.1698864","article-title":"A predictive model for decreasing clinical no-show rates in a primary care setting","volume":"14","author":"Ahmad","year":"2021","journal-title":"Int. J. Healthc. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s11628-020-00415-8","article-title":"A service analytic approach to studying patient no-shows","volume":"14","author":"Nasir","year":"2020","journal-title":"Serv. Bus."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/24725579.2020.1858210","article-title":"A multi-stage predictive model for missed appointments at outpatient primary care settings serving rural areas","volume":"11","author":"Wang","year":"2021","journal-title":"IISE Trans. Healthc. Syst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102496","DOI":"10.1016\/j.healthplace.2020.102496","article-title":"Using machine learning tools to investigate factors associated with trends in \u2018no-shows\u2019 in outpatient appointments","volume":"67","author":"Incze","year":"2021","journal-title":"Health Place"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.dsm.2021.06.002","article-title":"Machine learning-based prediction models for patients no-show in online outpatient appointments","volume":"2","author":"Fan","year":"2021","journal-title":"Data Sci. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.2214\/AJR.19.22594","article-title":"Artificial intelligence predictive analytics in the management of outpatient MRI appointment no-shows","volume":"215","author":"Chong","year":"2020","journal-title":"Am. J. Roentgenol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2376391","DOI":"10.1155\/2021\/2376391","article-title":"Efficient Prediction of Missed Clinical Appointment Using Machine Learning","volume":"2021","author":"Qureshi","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_11","first-page":"533","article-title":"The Prediction of Outpatient No-Show Visits by using Deep Neural Network from Large Data","volume":"11","author":"Alshammari","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_12","first-page":"29","article-title":"Machine-learning-based no show prediction in outpatient visits","volume":"4","author":"Elvira","year":"2018","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s40537-020-00384-9","article-title":"Predictors of outpatients\u2019 no-show: Big data analytics using Apache Spark","volume":"7","author":"Daghistani","year":"2020","journal-title":"J. Big Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"373","DOI":"10.5144\/0256-4947.2019.373","article-title":"Prediction of hospital no-show appointments through artificial intelligence algorithms","volume":"39","author":"AlMuhaideb","year":"2019","journal-title":"Ann. Saudi Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1080\/24725579.2019.1649764","article-title":"A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments","volume":"9","author":"Ahmadi","year":"2019","journal-title":"IISE Trans. Healthc. Syst. Eng."},{"key":"ref_16","first-page":"234","article-title":"Developing a Predictive Model of Predicting Appointment No-Show by Using Machine Learning Algorithms","volume":"12","author":"Alshammari","year":"2021","journal-title":"J. Adv. Inf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Almeida, R., Silva, N.A., and Vasconcelos, A. (2021, January 11\u201313). A Machine Learning Approach for Real Time Prediction of Last Minute Medical Appointments No-shows. Proceedings of the HEALTHINF, Vienna, Austria.","DOI":"10.5220\/0010221903280336"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Salazar, L.H., Fernandes, A.M., Dazzi, R., Raduenz, J., Garcia, N.M., and Leithardt, V.R. (2020, January 24\u201327). Prediction of attendance at medical appointments based on machine learning. Proceedings of the 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), Seville, Spain.","DOI":"10.23919\/CISTI49556.2020.9140973"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ferreira, I., and Vasconcelos, A. (2019, January 22\u201324). MedClick: Last Minute Medical Appointments No-Show Management. Proceedings of the HEALTHINF, Prague, Czech Republic.","DOI":"10.5220\/0007260702060215"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alshaya, S., McCarren, A., and Al-Rasheed, A. (2019, January 10\u201312). Predicting no-show medical appointments using machine learning. Proceedings of the International Conference on Computing, Riyadh, Saudi Arabia.","DOI":"10.1007\/978-3-030-36365-9_18"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moharram, A., Altamimi, S., and Alshammari, R. (2021, January 6\u20137). Data Analytics and Predictive Modeling for Appointments No-show at a Tertiary Care Hospital. Proceedings of the 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), Riyadh, Saudi Arabia.","DOI":"10.1109\/CAIDA51941.2021.9425258"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.eswa.2018.02.022","article-title":"Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework","volume":"102","author":"Srinivas","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113398","DOI":"10.1016\/j.dss.2020.113398","article-title":"Improving healthcare access management by predicting patient no-show behaviour","volume":"138","author":"Ferro","year":"2020","journal-title":"Decis. Support Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104290","DOI":"10.1016\/j.ijmedinf.2020.104290","article-title":"Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: A data analytics approach","volume":"145","author":"Srinivas","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_25","first-page":"293","article-title":"Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic","volume":"2020","author":"Chen","year":"2020","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Salazar, L.H.A., Leithardt, V.R., Parreira, W.D., da Rocha Fernandes, A.M., Barbosa, J.L.V., and Correia, S.D. (2021). Application of machine learning techniques to predict a patient\u2019s no-show in the healthcare sector. Future Internet, 14.","DOI":"10.3390\/fi14010003"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sestrem Och\u00f4a, I., Silva, L.A., de Mello, G., Alves da Silva, B., de Paz, J.F., Villarrubia Gonz\u00e1lez, G., Garcia, N.M., and Reis Quietinho Leithardt, V. (2019). PRICHAIN: A Partially Decentralized Implementation of UbiPri Middleware Using Blockchain. Sensors, 19.","DOI":"10.3390\/s19204483"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lopes, H., Pires, I.M., S\u00e1nchez San Blas, H., Garc\u00eda-Ovejero, R., and Leithardt, V. (2020). PriADA: Management and Adaptation of Information Based on Data Privacy in Public Environments. Computers, 9.","DOI":"10.3390\/computers9040077"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Verri Lucca, A., Augusto Silva, L., Luchtenberg, R., Garcez, L., Mao, X., Garc\u00eda Ovejero, R., Miguel Pires, I., Luis Vict\u00f3ria Barbosa, J., and Reis Quietinho Leithardt, V. (2020). A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information. Sensors, 20.","DOI":"10.3390\/s20216030"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100895","DOI":"10.1016\/j.softx.2021.100895","article-title":"PADRES: Tool for PrivAcy, Data REgulation and Security","volume":"17","author":"Pereira","year":"2022","journal-title":"SoftwareX"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/11\/507\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:48Z","timestamp":1760144448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/11\/507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,22]]},"references-count":30,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["info13110507"],"URL":"https:\/\/doi.org\/10.3390\/info13110507","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202209.0014.v1","asserted-by":"object"}]},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,22]]}}}