{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:14:22Z","timestamp":1781644462537,"version":"3.54.5"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T00:00:00Z","timestamp":1738972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ajman University","award":["2024-IRG-ENIT-16"],"award-info":[{"award-number":["2024-IRG-ENIT-16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg\/dL, a root mean square error (RMSE) of 15.36 mg\/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring.<\/jats:p>","DOI":"10.3390\/a18020095","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T12:12:16Z","timestamp":1739362336000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhancing Non-Invasive Blood Glucose Prediction from Photoplethysmography Signals via Heart Rate Variability-Based Features Selection Using Metaheuristic Algorithms"],"prefix":"10.3390","volume":"18","author":[{"given":"Saifeddin","family":"Alghlayini","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1980-1791","authenticated-orcid":false,"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates"},{"name":"Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3344-6127","authenticated-orcid":false,"given":"Mohamed","family":"Atef","sequence":"additional","affiliation":[{"name":"Department of Electrical and Communication Engineering, Collage of Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization: WHO (2024, November 09). World Health Organization: WHO Diabetes. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes."},{"key":"ref_2","unstructured":"(2024, November 09). International Diabetes Federation What Is Diabetes|International Federation of Diabetes. 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