{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:11:26Z","timestamp":1787166686107,"version":"build-2736575974"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"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>An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient\u2019s health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient\u2019s condition accurately and sends it to the doctor\u2019s portal.<\/jats:p>","DOI":"10.3390\/s23239558","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T08:36:59Z","timestamp":1701419819000},"page":"9558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8769-1358","authenticated-orcid":false,"given":"Sana","family":"Maqbool","sequence":"first","affiliation":[{"name":"Department of Computer Science, COMSATS University, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5161-6441","authenticated-orcid":false,"given":"Imran Sarwar","family":"Bajwa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0805-2398","authenticated-orcid":false,"given":"Saba","family":"Maqbool","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-6667","authenticated-orcid":false,"given":"Shabana","family":"Ramzan","sequence":"additional","affiliation":[{"name":"Department of Computer Science & IT, GSCWU Bahawalpur, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Junaid","family":"Chishty","sequence":"additional","affiliation":[{"name":"Shaukat Khanam Memorial Cancer Hospital and Research Center, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"ref_1","first-page":"141","article-title":"blood glucose level monitoring by noninvasive method using near infra red sensor","volume":"106","author":"Daarani","year":"2017","journal-title":"Int. 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