{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:22:04Z","timestamp":1780550524830,"version":"3.54.1"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The prediction of heart failure survivors is a challenging task and helps medical professionals to make the right decisions about patients. Expertise and experience of medical professionals are required to care for heart failure patients. Machine Learning models can help with understanding symptoms of cardiac disease. However, manual feature engineering is challenging and requires expertise to select the appropriate technique. This study proposes a smart healthcare framework using the Internet-of-Things (IoT) and cloud technologies that improve heart failure patients\u2019 survival prediction without considering manual feature engineering. The smart IoT-based framework monitors patients on the basis of real-time data and provides timely, effective, and quality healthcare services to heart failure patients. The proposed model also investigates deep learning models in classifying heart failure patients as alive or deceased. The framework employs IoT-based sensors to obtain signals and send them to the cloud web server for processing. These signals are further processed by deep learning models to determine the state of patients. Patients\u2019 health records and processing results are shared with a medical professional who will provide emergency help if required. The dataset used in this study contains 13 features and was attained from the UCI repository known as Heart Failure Clinical Records. The experimental results revealed that the CNN model is superior to other deep learning and machine learning models with a 0.9289 accuracy value.<\/jats:p>","DOI":"10.3390\/s22072431","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T23:30:23Z","timestamp":1647991823000},"page":"2431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["IoT Based Smart Monitoring of Patients\u2019 with Acute Heart Failure"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6015-9326","authenticated-orcid":false,"given":"Muhammad","family":"Umer","sequence":"first","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"},{"name":"Department of Computer Science Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saima","family":"Sadiq","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-2692","authenticated-orcid":false,"given":"Hanen","family":"Karamti","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2650-1703","authenticated-orcid":false,"given":"Walid","family":"Karamti","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"},{"name":"Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax 3052, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3161-6364","authenticated-orcid":false,"given":"Rizwan","family":"Majeed","sequence":"additional","affiliation":[{"name":"Directorate of Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2517-2867","authenticated-orcid":false,"given":"Michele","family":"NAPPI","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, 84084 Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"The internet of things in healthcare: An overview","volume":"1","author":"Yuehong","year":"2016","journal-title":"J. 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