{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:18:07Z","timestamp":1780481887432,"version":"3.54.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:00:00Z","timestamp":1779580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01324-w","type":"journal-article","created":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T08:34:26Z","timestamp":1779611666000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Design An Effective Diabetic Detection System With Cuckoo Search Optimization and Enhanced Recurrent Neural Network Using Multi-Modal Dataset"],"prefix":"10.1007","volume":"19","author":[{"given":"Sultan","family":"Alasmari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghanshyam G.","family":"Tejani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunil Kumar","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,24]]},"reference":[{"issue":"9","key":"1324_CR1","doi-asserted-by":"publisher","first-page":"2649","DOI":"10.3390\/s20092649","volume":"20","author":"AU Haq","year":"2020","unstructured":"Haq, A.U., Li, J.P., Khan, J., Memon, M.H., Nazir, S., Ahmad, S., Khan, G.A., Ali, A.: Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data. Sensors. 20(9), 2649 (2020). https:\/\/doi.org\/10.3390\/s20092649","journal-title":"Sensors"},{"key":"1324_CR2","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.future.2018.10.014","volume":"93","author":"TN Gia","year":"2019","unstructured":"Gia, T.N., Dhaou, I.B., Ali, M., Rahmani, A.M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Generation Comput. Syst. 93, 198\u2013211 (2019). https:\/\/doi.org\/10.1016\/j.future.2018.10.014","journal-title":"Future Generation Comput. Syst."},{"issue":"7","key":"1324_CR3","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1038\/s41574-020-0355-7","volume":"16","author":"DL Eizirik","year":"2020","unstructured":"Eizirik, D.L., Pasquali, L., Cnop, M.: Pancreatic \u03b2-cells in type 1 and type 2 diabetes mellitus: Different pathways to failure. Nat. Reviews Endocrinol. 16(7), 349\u2013362 (2020). https:\/\/doi.org\/10.1038\/s41574-020-0355-7","journal-title":"Nat. Reviews Endocrinol."},{"issue":"1","key":"1324_CR4","doi-asserted-by":"publisher","first-page":"11981","DOI":"10.1038\/s41598-020-68771-z","volume":"10","author":"L Kopitar","year":"2020","unstructured":"Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., Stiglic, G.: Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci. Rep. 10(1), 11981 (2020). https:\/\/doi.org\/10.1038\/s41598-020-68771-z","journal-title":"Sci. Rep."},{"issue":"1","key":"1324_CR5","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s40200-020-00520-5","volume":"19","author":"H Naz","year":"2020","unstructured":"Naz, H., Ahuja, S.: Deep learning approach for diabetes prediction using PIMA Indian dataset. J. Diabetes Metabolic Disorders. 19(1), 391\u2013403 (2020). https:\/\/doi.org\/10.1007\/s40200-020-00520-5","journal-title":"J. Diabetes Metabolic Disorders"},{"issue":"2","key":"1324_CR6","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/JBHI.2019.2891977","volume":"24","author":"K Li","year":"2019","unstructured":"Li, K., Daniels, J., Liu, C., Herrero, P., Georgiou, P.: Convolutional recurrent neural networks for glucose prediction. IEEE J. Biomedical Health Inf. 24(2), 603\u2013613 (2019). https:\/\/doi.org\/10.1109\/JBHI.2019.2891977","journal-title":"IEEE J. Biomedical Health Inf."},{"issue":"3","key":"1324_CR7","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/S2213-8587(19)30360-7","volume":"8","author":"JM Norris","year":"2020","unstructured":"Norris, J.M., Johnson, R.K., Stene, L.C.: Type 1 diabetes\u2014Early life origins and changing epidemiology. Lancet Diabetes Endocrinol. 8(3), 226\u2013238 (2020). https:\/\/doi.org\/10.1016\/S2213-8587(19)30360-7","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"1","key":"1324_CR8","first-page":"12","volume":"1","author":"S Hopek","year":"2020","unstructured":"Hopek, S., Siniak, G.: Diabetic neuropathy: New perspectives on early diagnosis and treatments. J. Curr. Diabetes Rep. 1(1), 12\u201314 (2020)","journal-title":"J. Curr. Diabetes Rep."},{"issue":"7811","key":"1324_CR9","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/s41586-020-2263-3","volume":"582","author":"CN Spracklen","year":"2020","unstructured":"Spracklen, C.N., Horikoshi, M., Kim, Y.J., Lin, K., Bragg, F., Moon, S., Suzuki, K., Tam, C.H., Tabara, Y., Kwak, S.H., Takeuchi, F.: Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature. 582(7811), 240\u2013245 (2020). https:\/\/doi.org\/10.1038\/s41586-020-2263-3","journal-title":"Nature"},{"key":"1324_CR10","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/j.procs.2020.03.336","volume":"167","author":"NP Tigga","year":"2020","unstructured":"Tigga, N.P., Garg, S.: Prediction of type 2 diabetes using machine learning classification methods. Procedia Comput. Sci. 167, 706\u2013716 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.03.336","journal-title":"Procedia Comput. Sci."},{"key":"1324_CR11","doi-asserted-by":"publisher","unstructured":"Arram, A., Ayob, M., Albadr, M.A.A., Albashish, D., Sulaiman, A.: A hybrid of an automated multi-filter with a spatial bound particle swarm optimization for gene selection and cancer classification. Heliyon. 11(5) (2025). https:\/\/doi.org\/10.1016\/j.heliyon.2025.e37778","DOI":"10.1016\/j.heliyon.2025.e37778"},{"issue":"15","key":"1324_CR12","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1056\/NEJMoa2004967","volume":"383","author":"CP Cannon","year":"2020","unstructured":"Cannon, C.P., Pratley, R., Dagogo-Jack, S., Mancuso, J., Huyck, S., Masiukiewicz, U., Charbonnel, B., Frederich, R., Gallo, S., Cosentino, F., Shih, W.J.: Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N. Engl. J. Med. 383(15), 1425\u20131435 (2020). https:\/\/doi.org\/10.1056\/NEJMoa2004967","journal-title":"N. Engl. J. Med."},{"issue":"4","key":"1324_CR13","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/S2213-8587(20)30026-7","volume":"8","author":"N Tofte","year":"2020","unstructured":"Tofte, N., Lindhardt, M., Adamova, K., Bakker, S.J., Beige, J., Beulens, J.W., Birkenfeld, A.L., Currie, G., Delles, C., Dimos, I., Francov\u00e1, L.: Early detection of diabetic kidney disease by urinary proteomics and subsequent intervention with spironolactone to delay progression (PRIORITY). Lancet Diabetes Endocrinol. 8(4), 301\u2013312 (2020). https:\/\/doi.org\/10.1016\/S2213-8587(20)30026-7","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"2","key":"1324_CR14","doi-asserted-by":"publisher","first-page":"117","DOI":"10.2174\/1570161117666190916141539","volume":"18","author":"C Faselis","year":"2020","unstructured":"Faselis, C., Katsimardou, A., Imprialos, K., Deligkaris, P., Kallistratos, M., Dimitriadis, K.: Microvascular complications of type 2 diabetes mellitus. Curr. Vasc. Pharmacol. 18(2), 117\u2013124 (2020). https:\/\/doi.org\/10.2174\/1570161117666190916141539","journal-title":"Curr. Vasc. Pharmacol."},{"key":"1324_CR15","doi-asserted-by":"publisher","unstructured":"Islam, M.M., Ferdousi, R., Rahman, S., Bushra, H.Y.: Likelihood prediction of diabetes at early stage using data mining techniques. In: Computer Vision and Machine Intelligence in Medical Image Analysis Springer. 113\u2013125 (2020). https:\/\/doi.org\/10.1007\/978-981-13-8798-2_11","DOI":"10.1007\/978-981-13-8798-2_11"},{"key":"1324_CR16","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ijcce.2021.01.001","volume":"2","author":"S Kumari","year":"2021","unstructured":"Kumari, S., Kumar, D., Mittal, M.: An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int. J. Cogn. Comput. Eng. 2, 40\u201346 (2021). https:\/\/doi.org\/10.1016\/j.ijcce.2021.01.001","journal-title":"Int. J. Cogn. Comput. Eng."},{"issue":"4","key":"1324_CR17","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MCOM.2018.1700789","volume":"56","author":"M Chen","year":"2018","unstructured":"Chen, M., Yang, J., Zhou, J., Hao, Y., Zhang, J., Youn, C.H.: 5G-smart diabetes: Toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun. Mag. 56(4), 16\u201323 (2018). https:\/\/doi.org\/10.1109\/MCOM.2018.1700789","journal-title":"IEEE Commun. Mag."},{"issue":"1\u20132","key":"1324_CR18","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1108\/ACI-01-2019-0005","volume":"18","author":"H Kaur","year":"2020","unstructured":"Kaur, H., Kumari, V.: Predictive modelling and analytics for diabetes using a machine learning approach. Appl. Comput. Inf. 18(1\u20132), 90\u2013100 (2020). https:\/\/doi.org\/10.1108\/ACI-01-2019-0005","journal-title":"Appl. Comput. Inf."},{"issue":"3","key":"1324_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1049\/htl2.12008","volume":"8","author":"J Ramesh","year":"2021","unstructured":"Ramesh, J., Aburukba, R., Sagahyroon, A.: A remote healthcare monitoring framework for diabetes prediction using machine learning. Healthc. Technol. Lett. 8(3), 45\u201357 (2021). https:\/\/doi.org\/10.1049\/htl2.12008","journal-title":"Healthc. Technol. Lett."},{"issue":"Suppl 1","key":"1324_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-017-1534-0","volume":"22","author":"N Yuvaraj","year":"2019","unstructured":"Yuvaraj, N., SriPreethaa, K.R.: Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster. Cluster Comput. 22(Suppl 1), 1\u20139 (2019). https:\/\/doi.org\/10.1007\/s10586-017-1534-0","journal-title":"Cluster Comput."},{"key":"1324_CR21","doi-asserted-by":"publisher","unstructured":"Butt, U.M., Letchmunan, S., Ali, M., Hassan, F.H., Baqir, A., Sherazi, H.H.R.: Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021, Article ID 9937809. (2021). https:\/\/doi.org\/10.1155\/2021\/9937809","DOI":"10.1155\/2021\/9937809"},{"issue":"1","key":"1324_CR22","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s40537-019-0175-6","volume":"6","author":"N Sneha","year":"2019","unstructured":"Sneha, N., Gangil, T.: Analysis of diabetes mellitus for early prediction using optimal feature selection. J. Big Data. 6(1), 13 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0175-6","journal-title":"J. Big Data"},{"issue":"4","key":"1324_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJHISI.20211001.oa1","volume":"16","author":"P Nagaraj","year":"2021","unstructured":"Nagaraj, P., Deepalakshmi, P.: Diabetes prediction using enhanced SVM and deep neural network learning techniques: An algorithmic approach for early screening of diabetes. Int. J. Healthc. Inform. Syst. Inf. 16(4), 1\u201320 (2021). https:\/\/doi.org\/10.4018\/IJHISI.20211001.oa1","journal-title":"Int. J. Healthc. Inform. Syst. Inf."},{"key":"1324_CR24","doi-asserted-by":"publisher","first-page":"73029","DOI":"10.1109\/ACCESS.2021.3081538","volume":"9","author":"M Shokrekhodaei","year":"2021","unstructured":"Shokrekhodaei, M., Cistola, D.P., Roberts, R.C., Quinones, S.: Non-invasive glucose monitoring using optical sensor and machine learning techniques for diabetes applications. IEEE Access. 9, 73029\u201373045 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3081538","journal-title":"IEEE Access."},{"issue":"4","key":"1324_CR25","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.icte.2018.10.005","volume":"4","author":"G Swapna","year":"2018","unstructured":"Swapna, G., Vinayakumar, R., Soman, K.P.: Diabetes detection using deep learning algorithms. ICT Express. 4(4), 243\u2013246 (2018). https:\/\/doi.org\/10.1016\/j.icte.2018.10.005","journal-title":"ICT Express"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01324-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01324-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01324-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:53:13Z","timestamp":1780480393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01324-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,24]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1324"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01324-w","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,24]]},"assertion":[{"value":"6 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to participate"}}],"article-number":"217"}}