{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:49:44Z","timestamp":1787028584796,"version":"build-2736575974"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was funded by PDD Grant of Ristekdikti, grant number: NKB-334\/UN2.RST\/HKP.05.00\/2021.","award":["NKB-334\/UN2.RST\/HKP.05.00\/2021"],"award-info":[{"award-number":["NKB-334\/UN2.RST\/HKP.05.00\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, \u201cnormal\u201d and \u201cdiabetes\u201d. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%.<\/jats:p>","DOI":"10.3390\/info13020059","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:40:18Z","timestamp":1643143218000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal"],"prefix":"10.3390","volume":"13","author":[{"given":"Ernia","family":"Susana","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Universitas Indonesia, Depok 16424, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0374-4465","authenticated-orcid":false,"given":"Kalamullah","family":"Ramli","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Universitas Indonesia, Depok 16424, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2722-5763","authenticated-orcid":false,"given":"Hendri","family":"Murfi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Universitas Indonesia, Depok 16424, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nursama Heru","family":"Apriantoro","sequence":"additional","affiliation":[{"name":"Department of Radiodiagnostic and Radiotherapy, Poltekkes Kemenkes Jakarta II, Jakarta 12120, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1080\/09674845.2012.12002443","article-title":"A history of blood glucose meters and their role in self-monitoring of diabetes mellitus","volume":"69","author":"Clarke","year":"2012","journal-title":"Br. 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