{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:46:06Z","timestamp":1778168766931,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11042-022-13582-9","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T16:03:08Z","timestamp":1659715388000},"page":"6221-6241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes"],"prefix":"10.1007","volume":"82","author":[{"given":"Marmik","family":"Shrestha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar Hisham","family":"Alsadoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-3540","authenticated-orcid":false,"given":"Abeer","family":"Alsadoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thair","family":"Al-Dala\u2019in","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarik A.","family":"Rashid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. W. C.","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Alrubaie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"issue":"1","key":"13582_CR1","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1109\/JBHI.2019.2899218","volume":"24","author":"M Bernardini","year":"2020","unstructured":"Bernardini M, Romeo L, Misericordia P, Frontoni E (2020) Discovering the type 2 diabetes in electronic health records using the sparse balanced support vector machine. IEEE J Biomed Health Inform 24(1):235\u2013246. https:\/\/doi.org\/10.1109\/JBHI.2019.2899218","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"13582_CR2","doi-asserted-by":"publisher","first-page":"e3252","DOI":"10.1002\/dmrr.3252","volume":"36","author":"A Cahn","year":"2020","unstructured":"Cahn A et al (2020) Prediction of progression from pre-diabetes to diabetes: development and validation of a machine learning model. Diabetes Metab Res Rev 36(2):e3252. https:\/\/doi.org\/10.1002\/dmrr.3252","journal-title":"Diabetes Metab Res Rev"},{"key":"13582_CR3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.engappai.2017.09.002","volume":"67","author":"A Caliskan","year":"2018","unstructured":"Caliskan A, Yuksel ME, Badem H, Basturk A (2018) Performance improvement of deep neural network classifiers by a simple training strategy. Eng Appl Artif Intell 67:14\u201323. https:\/\/doi.org\/10.1016\/j.engappai.2017.09.002","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"13582_CR4","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3349\/ymj.2019.60.2.191","volume":"60","author":"BG Choi","year":"2019","unstructured":"Choi BG, Rha SW, Kim SW, Kang JH, Park JY, Noh YK (2019) Machine learning for the prediction of new-onset diabetes mellitus during 5-year follow-up in non-diabetic patients with cardiovascular risks. Yonsei Med J 60(2):191\u2013199. https:\/\/doi.org\/10.3349\/ymj.2019.60.2.191","journal-title":"Yonsei Med J"},{"key":"13582_CR5","doi-asserted-by":"publisher","unstructured":"Gadekallu TR et al (2020) Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics 9(2). https:\/\/doi.org\/10.3390\/electronics9020274","DOI":"10.3390\/electronics9020274"},{"issue":"12","key":"13582_CR6","doi-asserted-by":"publisher","first-page":"e0225900","DOI":"10.1371\/journal.pone.0225900","volume":"14","author":"R Gast","year":"2019","unstructured":"Gast R, Rose D, Salomon C, Moller HE, Weiskopf N, Knosche TR (2019) PyRates-A Python framework for rate-based neural simulations. PLoS ONE 14(12):e0225900. https:\/\/doi.org\/10.1371\/journal.pone.0225900","journal-title":"PLoS ONE"},{"issue":"5","key":"13582_CR7","doi-asserted-by":"publisher","first-page":"304","DOI":"10.5539\/gjhs.v7n5p304","volume":"7","author":"S Habibi","year":"2015","unstructured":"Habibi S, Ahmadi M, Alizadeh S (2015) Type 2 diabetes mellitus screening and risk factors using decision tree: results of data mining. Glob J Health Sci 7(5):304\u2013310. https:\/\/doi.org\/10.5539\/gjhs.v7n5p304","journal-title":"Glob J Health Sci"},{"issue":"2","key":"13582_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.5815\/ijieeb.2019.02.03","volume":"11","author":"S Islam Ayon","year":"2019","unstructured":"Islam Ayon S, Milon Islam M (2019) Diabetes prediction: a deep learning approach. Int J Inf Eng Electron Bus 11(2):21\u201327. https:\/\/doi.org\/10.5815\/ijieeb.2019.02.03","journal-title":"Int J Inf Eng Electron Bus"},{"issue":"4","key":"13582_CR9","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.cegh.2018.12.004","volume":"7","author":"K Kannadasan","year":"2019","unstructured":"Kannadasan K, Edla DR, Kuppili V (2019) Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clin Epidemiol Glob Health 7(4):530\u2013535. https:\/\/doi.org\/10.1016\/j.cegh.2018.12.004","journal-title":"Clin Epidemiol Glob Health"},{"issue":"1","key":"13582_CR10","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/JBHI.2015.2396520","volume":"20","author":"BJ Lee","year":"2016","unstructured":"Lee BJ, Kim JY (2016) Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J Biomed Health Inform 20(1):39\u201346. https:\/\/doi.org\/10.1109\/JBHI.2015.2396520","journal-title":"IEEE J Biomed Health Inform"},{"key":"13582_CR11","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1016\/j.procs.2017.08.193","volume":"112","author":"F Mercaldo","year":"2017","unstructured":"Mercaldo F, Nardone V, Santone A (2017) Diabetes mellitus affected patients classification and diagnosis through machine learning techniques. Proc Comput Sci 112:2519\u20132528. https:\/\/doi.org\/10.1016\/j.procs.2017.08.193","journal-title":"Proc Comput Sci"},{"key":"13582_CR12","doi-asserted-by":"publisher","first-page":"2896","DOI":"10.1109\/EMBC.2017.8037462","volume":"2017","author":"A Mohebbi","year":"2017","unstructured":"Mohebbi A, Aradottir TB, Johansen AR, Bengtsson H, Fraccaro M, Morup M (2017) A deep learning approach to adherence detection for type 2 diabetics. Conf Proc IEEE Eng Med Biol Soc 2017:2896\u20132899. https:\/\/doi.org\/10.1109\/EMBC.2017.8037462","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"13582_CR13","doi-asserted-by":"publisher","first-page":"105055","DOI":"10.1016\/j.cmpb.2019.105055","volume":"182","author":"BP Nguyen","year":"2019","unstructured":"Nguyen BP, Pham HN, Tran H, Nghiem N, Nguyen QH, Do TTT, Tran CT, Simpson CR (2019) Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Comput Methods Prog Biomed 182:105055. https:\/\/doi.org\/10.1016\/j.cmpb.2019.105055","journal-title":"Comput Methods Prog Biomed"},{"issue":"2","key":"13582_CR14","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1177\/1460458216663023","volume":"24","author":"A Pimentel","year":"2018","unstructured":"Pimentel A, Carreiro AV, Ribeiro RT, Gamboa H (2018) Screening diabetes mellitus 2 based on electronic health records using temporal features. Health Informatics J 24(2):194\u2013205. https:\/\/doi.org\/10.1177\/1460458216663023","journal-title":"Health Informatics J"},{"key":"13582_CR15","doi-asserted-by":"publisher","unstructured":"Raschka S, Patterson J, Nolet C (2020) Machine learning in python: main developments and technology trends in data science, machine learning, and artificial intelligence. Information 11(4). https:\/\/doi.org\/10.3390\/info11040193","DOI":"10.3390\/info11040193"},{"key":"13582_CR16","doi-asserted-by":"publisher","unstructured":"Ryu KS, Lee SW, Batbaatar E, Lee JW, Choi KS, Cha HS (2020) A deep learning model for estimation of patients with undiagnosed diabetes. Appl Sci 10(1). https:\/\/doi.org\/10.3390\/app10010421","DOI":"10.3390\/app10010421"},{"key":"13582_CR17","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.cmpb.2018.01.004","volume":"157","author":"P Samant","year":"2018","unstructured":"Samant P, Agarwal R (2018) Machine learning techniques for medical diagnosis of diabetes using iris images. Comput Methods Prog Biomed 157:121\u2013128. https:\/\/doi.org\/10.1016\/j.cmpb.2018.01.004","journal-title":"Comput Methods Prog Biomed"},{"key":"13582_CR18","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/j.procs.2020.03.336","volume":"167","author":"NP Tigga","year":"2020","unstructured":"Tigga NP, Garg S (2020) Prediction of type 2 diabetes using machine learning classification methods. Proc Comput Sci 167:706\u2013716. https:\/\/doi.org\/10.1016\/j.procs.2020.03.336","journal-title":"Proc Comput Sci"},{"key":"13582_CR19","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.imu.2017.12.006","volume":"10","author":"H Wu","year":"2018","unstructured":"Wu H, Yang S, Huang Z, He J, Wang X (2018) Type 2 diabetes mellitus prediction model based on data mining. Inform Med Unlocked 10:100\u2013107. https:\/\/doi.org\/10.1016\/j.imu.2017.12.006","journal-title":"Inform Med Unlocked"},{"key":"13582_CR20","doi-asserted-by":"publisher","first-page":"E130","DOI":"10.5888\/pcd16.190109","volume":"16","author":"Z Xie","year":"2019","unstructured":"Xie Z, Nikolayeva O, Luo J, Li D (2019) Building risk prediction models for type 2 diabetes using machine learning techniques. Prev Chronic Dis 16:E130. https:\/\/doi.org\/10.5888\/pcd16.190109","journal-title":"Prev Chronic Dis"},{"key":"13582_CR21","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ijmedinf.2016.09.014","volume":"97","author":"T Zheng","year":"2017","unstructured":"Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y (2017) A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform 97:120\u2013127. https:\/\/doi.org\/10.1016\/j.ijmedinf.2016.09.014","journal-title":"Int J Med Inform"},{"key":"13582_CR22","doi-asserted-by":"publisher","first-page":"515","DOI":"10.3389\/fgene.2018.00515","volume":"9","author":"Q Zou","year":"2018","unstructured":"Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H (2018) Predicting diabetes mellitus with machine learning techniques. Front Genet 9:515. https:\/\/doi.org\/10.3389\/fgene.2018.00515","journal-title":"Front Genet"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13582-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13582-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13582-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T08:31:24Z","timestamp":1674635484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13582-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,5]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["13582"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13582-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,5]]},"assertion":[{"value":"12 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflicts of interests as well.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}