{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T10:05:55Z","timestamp":1767348355230,"version":"3.48.0"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Healthcare professionals face numerous challenges when analyzing data and providing treatment, including determining which parameters to measure, the frequency of measurement, i.e., how frequently to measure them, and the responsibility for monitoring patient health with new medical devices. Machine learning (ML) techniques are efficient predictive models used to improve early prediction of patient care and reduce the cost of implementing healthcare systems. This study proposes a new model (data prediction and labeling using a negative feature based on a multi-agent system (PLPF-MAS)) that provides a smart city-based healthcare system for the continuous monitoring of patients\u2019 vital signs, such as heart rate, blood pressure, respiratory rate, and blood oxygen saturation. It also predicts future states and provides suitable recommendations based on clinical events. The MIMIC-II database of the MIT physio bank archive is used, which contains 1023 patient records. Additionally, the EHR dataset is used, which contains 10,000 patient records. The models were trained and evaluated for six bio-signals. The PLPF-MAS model is distinguished from traditional methods in its advanced system, which combines the activities of several agents and the intelligent distribution of responsibilities among them. The LR agent measures the model\u2019s reliability in parallel with the AE-HMM agent to predict the Prisk; it then sends the data to a coordinator and a supervisory agent to monitor and manage the model. Our model is characterized by strong flexibility and reliability, the ability to deal with large datasets, and a short response time. It provides recommendations and warnings about risks, and it can predict clinical states with high accuracy. The new model achieved an accuracy of 98.4%, a precision of 95.3%, a sensitivity of 99.2%, a specificity of 99.1%, an F1-Score of 97.1%, and an R2 of 98%, when the MIMIC-II dataset was used. Conversely, it achieved an accuracy of 93%, a precision of 92%, a recall of 94%, an F1-Score of 93%, an AUC-ROC of 94%, and an AUC-PR of 89% when the EHR dataset was used.<\/jats:p>","DOI":"10.3390\/fi18010027","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T09:56:23Z","timestamp":1767347783000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for Assessing Vital Signs in Humans in Smart Cities Based on a Multi-Agent System"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2596-870X","authenticated-orcid":false,"given":"Nejood Faisal","family":"Abdulsattar","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65816, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7351-9397","authenticated-orcid":false,"given":"Hassan","family":"Khotanlou","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65816, Iran"}]},{"given":"Hatam","family":"Abdoli","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65816, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"ref_1","first-page":"11","article-title":"An intrusion detection system using a machine learning approach in IOT-based smart cities","volume":"13","author":"Nadu","year":"2023","journal-title":"J. Internet Serv. Inf. Secur. (JISIS)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.cvdhj.2023.05.001","article-title":"Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study","volume":"4","author":"Hautala","year":"2023","journal-title":"Cardiovasc. Digit. Health J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e12262","DOI":"10.1049\/tje2.12262","article-title":"IoT applications and challenges in smart cities and services","volume":"2023","author":"Rafiq","year":"2023","journal-title":"J. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4796","DOI":"10.1038\/s41598-025-89589-7","article-title":"Decentralized identifiers based IoT data trusted collection","volume":"15","author":"Zhang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102321","DOI":"10.1016\/j.techsoc.2023.102321","article-title":"Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research","volume":"74","author":"Zahlan","year":"2023","journal-title":"Technol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/ICJECE.2022.3220700","article-title":"Smart Health Systems Components, Challenges, and Opportunities","volume":"45","author":"Abdeen","year":"2022","journal-title":"IEEE Can. J. Electr. Comput. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102704","DOI":"10.1016\/j.scs.2020.102704","article-title":"Development of integrated sustainability performance indicators for better management of smart cities","volume":"67","author":"Dincer","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aljohani, F.H., Abi Sen, A.A., Ramazan, M.S., Alzahrani, B., and Bahbouh, N.M. (2023). A Smart Framework for Managing Natural Disasters Based on the IoT and ML. Appl. Sci., 13.","DOI":"10.3390\/app13063888"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41893-021-00799-z","article-title":"The social shortfall and ecological overshoot of nations","volume":"5","author":"Fanning","year":"2022","journal-title":"Nat. Sustain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104089","DOI":"10.1016\/j.scs.2022.104089","article-title":"Applications of ML\/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review","volume":"85","author":"Heidari","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102730","DOI":"10.1016\/j.scs.2021.102730","article-title":"Smart cities as large technological systems: Overcoming organizational challenges in smart cities through collective action","volume":"67","author":"Mondschein","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105112","DOI":"10.1016\/j.scs.2023.105112","article-title":"A systematic literature review of the smart city transformation process: The role and interaction of stakeholders and technology","volume":"101","author":"Dai","year":"2023","journal-title":"Sustain. Cities Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104859","DOI":"10.1016\/j.cities.2024.104859","article-title":"Smart city development: Data sharing vs. data protection legislations","volume":"148","author":"Joyce","year":"2024","journal-title":"Cities"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103164","DOI":"10.1016\/j.jnca.2021.103164","article-title":"A systematic review of IoT in healthcare: Applications, techniques, and trends","volume":"192","author":"Kashani","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.procs.2023.01.291","article-title":"The IoT to Smart Cities-A design science research approach","volume":"219","author":"Duque","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Prabakar, D., Sundarrajan, M., Manikandan, R., Jhanjhi, N.Z., Masud, M., and Alqhatani, A. (2023). Energy analysis-based cyber attack detection by IoT with artificial intelligence in a sustainable smart city. Sustainability, 15.","DOI":"10.3390\/su15076031"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Al Khatib, I., Shamayleh, A., and Ndiaye, M. (2024). Healthcare and the Internet of Medical Things: Applications, Trends, Key Challenges, and Proposed Resolutions. Informatics, 11.","DOI":"10.3390\/informatics11030047"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.53759\/7669\/jmc202101022","article-title":"Machine learning technique and applications\u2013an classification analysis","volume":"1","author":"Ge","year":"2021","journal-title":"J. Mach. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khan, U.T., and Zia, M.F. (2021, January 9\u201310). Smart city technologies, key components, and its aspects. Proceedings of the 2021 International Conference on Innovative Computing (ICIC), Lahore, Pakistan.","DOI":"10.1109\/ICIC53490.2021.9692989"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/smartcities4020024","article-title":"IoT in smart cities: A survey of technologies, practices and challenges","volume":"4","author":"Syed","year":"2021","journal-title":"Smart Cities"},{"key":"ref_21","first-page":"261","article-title":"Connecting the indispensable roles of iot and artificial intelligence in smart cities: A survey","volume":"2","author":"Nguyen","year":"2024","journal-title":"J. Inf. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cugurullo, F. (2020). Urban artificial intelligence: From automation to autonomy in the smart city. Front. Sustain. Cities, 2.","DOI":"10.3389\/frsc.2020.00038"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Echegaray, N., Hassoun, A., Jagtap, S., Tetteh-Caesar, M., Kumar, M., Tomasevic, I., Goksen, G., and Lorenzo, J.M. (2022). Meat 4.0: Principles and applications of industry 4.0 technologies in the meat industry. Appl. Sci., 12.","DOI":"10.3390\/app12146986"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.comcom.2020.02.069","article-title":"Applications of artificial intelligence and machine learning in smart cities","volume":"154","author":"Ullah","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e3958","DOI":"10.1002\/ett.3958","article-title":"Systematic review of Internet of Things in smart farming","volume":"31","author":"Terence","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e43014","DOI":"10.2196\/43014","article-title":"Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data","volume":"7","author":"Sezgin","year":"2023","journal-title":"JMIR Form. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127055","DOI":"10.1016\/j.jclepro.2021.127055","article-title":"Innovative blockchain-based farming marketplace and smart contract performance evaluation","volume":"306","author":"Leduc","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Navarro, E., Costa, N., and Pereira, A. (2020). A systematic review of IoT solutions for smart farming. Sensors, 20.","DOI":"10.3390\/s20154231"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kaliappan, V.K., Gnanamurthy, S., Yahya, A., Samikannu, R., Babar, M., Qureshi, B., and Koubaa, A. (2023). Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities. Sustainability, 15.","DOI":"10.3390\/su15065457"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mishra, P., and Singh, G. (2023). Internet of Medical Things Healthcare for Sustainable Smart Cities: Current Status and Future Prospects. Appl. Sci., 13.","DOI":"10.3390\/app13158869"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"67957","DOI":"10.1109\/ACCESS.2021.3077529","article-title":"Natural brain-inspired intelligence for screening in healthcare applications","volume":"9","author":"Naghshvarianjahromi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10318","DOI":"10.1109\/JIOT.2021.3052067","article-title":"A deep-learning-based smart healthcare system for patient\u2019s discomfort detection at the edge of Internet of Things","volume":"8","author":"Ahmed","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.irbm.2020.02.006","article-title":"Identification of human vital functions directly relevant to the respiratory system based on the cardiac and acoustic parameters and random forest","volume":"42","author":"Proniewska","year":"2021","journal-title":"IRBM"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/JTEHM.2023.3241635","article-title":"Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning","volume":"11","author":"Le","year":"2023","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.procs.2025.03.302","article-title":"Improving Health Care Analytics: LSTM Networks for Enhanced Risk Assessment","volume":"259","author":"Srivastava","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Varma, G., Chauhan, R., Singh, M., and Singh, D. (2020). Pre-emption of affliction severity using HRV measurements from a smart wearable; case-study on SARS-Cov-2 symptoms. Sensors, 20.","DOI":"10.3390\/s20247068"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012031","DOI":"10.1088\/1742-6596\/1921\/1\/012031","article-title":"Hidden Markov Model energy conservation approach for continuous monitoring of vital signs in geriatric care applications","volume":"1921","author":"Pillai","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.pmcj.2016.12.009","article-title":"PEACE-Home: Probabilisticestimation of abnormal clinical events using vital sign correlations for reliable home-based monitoring","volume":"38","author":"Forkan","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Al-Shamaileh, M., Anthony, P., and Charters, S. (2024). Agent-Based Trust and Reputation Model in Smart IoT Environments. Technologies, 12.","DOI":"10.3390\/technologies12110208"},{"key":"ref_40","first-page":"188","article-title":"Multiagent AI Systems in Health Care: Envisioning Next-Generation Intelligence","volume":"42","author":"Borkowski","year":"2025","journal-title":"Fed. Pract."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.69882\/adba.cem.2025011","article-title":"Collaborative Care: Multi-Agent Systems in Healthcare","volume":"2","author":"Power","year":"2025","journal-title":"Comput. Electron. Med."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pandey, H.G., Amod, A., and Kumar, S. (2024, January 16). Advancing healthcare automation: Multi-agent system for medical necessity justification. Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, Bangkok, Thailand.","DOI":"10.18653\/v1\/2024.bionlp-1.4"},{"key":"ref_43","first-page":"5293","article-title":"Random Forest Algorithm for Real-Time Health Monitoring Throught Iot Data","volume":"11","author":"Thorat","year":"2024","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tsai, C.L., Lu, T.C., Wang, C.H., Fang, C.C., Chen, W.J., and Huang, C.H. (2022). Trajectories of vital signs and risk of in-hospital cardiac arrest. Front. Med., 8.","DOI":"10.3389\/fmed.2021.800943"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100280","DOI":"10.1016\/j.dajour.2023.100280","article-title":"A machine learning-based decision support system for temporal human cognitive state estimation during online education using wearable physiological monitoring devices","volume":"8","author":"Gupta","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Alexan, A.I., Alexan, A.R., and Oniga, S. (2023). Real-time machine learning for human activities recognition based on wrist-worn wearable devices. Appl. Sci., 14.","DOI":"10.3390\/app14010329"},{"key":"ref_47","first-page":"21","article-title":"Electronic system to monitoring vital signs in pregnancy through Random Forests","volume":"12","year":"2021","journal-title":"Int. J. Comb. Optim. Probl. Inform."},{"key":"ref_48","unstructured":"Saeed, M., Villarroel, M., Reisner, A., Clifford, G., Lehman, L., Moody, G., Heldt, T., Kyaw, T., Moody, B., and Mark, R. (2011). MIMIC-II Clinical Database (Version 2.6.0), PhysioNet. RRID:SCR_007345."},{"key":"ref_49","unstructured":"Shahi, V. (2025, December 09). EHR Data [Data Set], Available online: https:\/\/www.kaggle.com\/datasets\/vipulshahi\/ehr-data."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/27\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T10:02:03Z","timestamp":1767348123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,2]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["fi18010027"],"URL":"https:\/\/doi.org\/10.3390\/fi18010027","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,2]]}}}