{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T03:16:13Z","timestamp":1769656573025,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement, and ambient temperature) were pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artifacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, Signal-to-Noise Ratio (SNR), energy consumption, and packet loss ratio (PLR). The two-level filtering strategy, i.e., LMS, ensures that only denoised and high-priority records are forwarded for more processing, enabling timely delivery for supporting the downstream clinical network by optimizing the communication. The proposed mechanism is evaluated via extensive simulations involving 30\u2013100 devices and multiple generations and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay by 10%, as well as communication latency and energy consumption, while improving the packet delivery ratio by approximately 15%, as well as throughput, SNR, and overall Quality of Service (QoS) by up to 98%. These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring, and timely intervention in smart healthcare environments.<\/jats:p>","DOI":"10.3390\/digital6010010","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T15:04:46Z","timestamp":1769612686000},"page":"10","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient and Automated Smart Healthcare System Using Genetic Algorithm and Two-Level Filtering Scheme"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4761-1912","authenticated-orcid":false,"given":"Geetanjali","family":"Rathee","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Netaji Subhas University of Technology, Dwarka Sector-3, New Delhi 110078, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2957-1491","authenticated-orcid":false,"given":"Hemraj","family":"Saini","sequence":"additional","affiliation":[{"name":"School of Computing, DIT University, Dehradun 248009, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-519X","authenticated-orcid":false,"given":"Chaker Abdelaziz","family":"Kerrache","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique et de Math\u00e9matiques, Universit\u00e9 Amar Telidji de Laghouat, Laghouat 03000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramzi","family":"Djemai","sequence":"additional","affiliation":[{"name":"School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-7848","authenticated-orcid":false,"given":"Mohamed Chahine","family":"Ghanem","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, University of Liverpool, Liverpool L69 7ZX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mazhar, T., Irfan, H.M., Haq, I., Ullah, I., Ashraf, M., Shloul, T.A., Ghadi, Y.Y., and Elkamchouchi, D.H. 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