{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T06:24:59Z","timestamp":1792650299945,"version":"4.5.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06481-x","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T11:07:56Z","timestamp":1631531276000},"page":"1613-1621","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A weighted ensemble classifier based on WOA for classification of diabetes"],"prefix":"10.1007","volume":"34","author":[{"given":"Fatemeh","family":"Khademi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3602-6125","authenticated-orcid":false,"given":"Mohsen","family":"Rabbani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Homayun","family":"Motameni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ebrahim","family":"Akbari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"issue":"13","key":"6481_CR1","doi-asserted-by":"publisher","first-page":"5402","DOI":"10.1016\/j.eswa.2013.04.003","volume":"40","author":"MW Aslam","year":"2013","unstructured":"Aslam MW, Zhu Z, Nandi AK (2013) Feature generation using genetic programming with comparative partner selection for diabetes classification. Exp Syst Appl 40(13):5402\u20135412","journal-title":"Exp Syst Appl"},{"key":"6481_CR2","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2000","unstructured":"Atlas D (2000) IDF diabetes atlas, 1st edn. International diabetes federation, Brussels","edition":"1"},{"key":"6481_CR3","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2003","unstructured":"Atlas D (2003) IDF diabetes atlas, 2nd edn. International diabetes federation, Brussels","edition":"2"},{"key":"6481_CR4","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2006","unstructured":"Atlas D (2006) IDF diabetes atlas, 3rd edn. International diabetes federation, Brussels","edition":"3"},{"key":"6481_CR5","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2009","unstructured":"Atlas D (2009) IDF diabetes atlas, 4th edn. International diabetes federation, Brussels","edition":"4"},{"key":"6481_CR6","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2011","unstructured":"Atlas D (2011) IDF diabetes atlas, 5th edn. International diabetes federation, Brussels","edition":"5"},{"key":"6481_CR7","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2013","unstructured":"Atlas D (2013) IDF diabetes atlas, 6th edn. International diabetes federation, Brussels","edition":"6"},{"key":"6481_CR8","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2015","unstructured":"Atlas D (2015) IDF diabetes atlas, 7th edn. International diabetes federation, Brussels","edition":"7"},{"key":"6481_CR9","volume-title":"IDF diabetes atlas","author":"D Atlas","year":"2017","unstructured":"Atlas D (2017) IDF diabetes atlas, 8th edn. International diabetes federation, Brussels","edition":"8"},{"key":"6481_CR10","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.eswa.2018.08.002","volume":"115","author":"JA Carter","year":"2019","unstructured":"Carter JA, Long CS, Smith BP, Smith TL, Donati GL (2019) Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes. Exp Syst Appl 115:245\u2013255","journal-title":"Exp Syst Appl"},{"key":"6481_CR11","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.neucom.2018.07.102","volume":"396","author":"J De La Torre","year":"2019","unstructured":"De La Torre J, Valls A, Puig D (2019) A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing 396:465\u2013476","journal-title":"Neurocomputing"},{"issue":"4","key":"6481_CR12","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1016\/j.dsp.2009.10.021","volume":"20","author":"E Dogantekin","year":"2010","unstructured":"Dogantekin E, Dogantekin A, Avci D, Avci L (2010) An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: Lda-anfis. Dig Signal Process 20(4):1248\u20131255","journal-title":"Dig Signal Process"},{"key":"6481_CR13","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.chaos.2015.11.029","volume":"83","author":"O Erkaymaz","year":"2016","unstructured":"Erkaymaz O, Ozer M (2016) Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes. Chaos, Solitons Fractals 83:178\u2013185","journal-title":"Chaos, Solitons Fractals"},{"issue":"12","key":"6481_CR14","doi-asserted-by":"publisher","first-page":"14650","DOI":"10.1016\/j.eswa.2011.05.018","volume":"38","author":"MF Ganji","year":"2011","unstructured":"Ganji MF, Abadeh MS (2011) A fuzzy classification system based on ant colony optimization for diabetes disease diagnosis. Exp Syst Appl 38(12):14650\u201314659","journal-title":"Exp Syst Appl"},{"key":"6481_CR15","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.eswa.2016.11.017","volume":"71","author":"MB Gorza\u0142czany","year":"2017","unstructured":"Gorza\u0142czany MB, Rudzi\u0144ski F (2017) Interpretable and accurate medical data classification-a multi-objective genetic-fuzzy optimization approach. Exp Syst Appl 71:26\u201339","journal-title":"Exp Syst Appl"},{"key":"6481_CR16","first-page":"1","volume":"32","author":"DJ Hemanth","year":"2019","unstructured":"Hemanth DJ, Deperlioglu O, Kose U (2019) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Appl 32:1\u201315","journal-title":"Neural Comput Appl"},{"issue":"1\u20132","key":"6481_CR17","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.eswa.2007.06.004","volume":"35","author":"H Kahramanli","year":"2008","unstructured":"Kahramanli H, Allahverdi N (2008) Design of a hybrid system for the diabetes and heart diseases. Exp Syst Appl 35(1\u20132):82\u201389","journal-title":"Exp Syst Appl"},{"key":"6481_CR18","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","volume":"15","author":"I Kavakiotis","year":"2017","unstructured":"Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I (2017) Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 15:104\u2013116","journal-title":"Comput Struct Biotechnol J"},{"key":"6481_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"6481_CR20","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.compbiomed.2019.03.017","volume":"108","author":"B Navaneeth","year":"2019","unstructured":"Navaneeth B, Suchetha M (2019) Pso optimized 1-d cnn-svm architecture for real-time detection and classification applications. Comput Biol Med 108:85\u201392","journal-title":"Comput Biol Med"},{"key":"6481_CR21","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1016\/j.ygeno.2019.01.006","volume":"111","author":"H Nematzadeh","year":"2019","unstructured":"Nematzadeh H, Enayatifar R, Mahmud M, Akbari E (2019) Frequency based feature selection method using whale algorithm. Genomics 111:1946\u20131955","journal-title":"Genomics"},{"issue":"1","key":"6481_CR22","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.bbe.2018.09.007","volume":"39","author":"N Nirala","year":"2019","unstructured":"Nirala N, Periyasamy R, Singh BK, Kumar A (2019) Detection of type-2 diabetes using characteristics of toe photoplethysmogram by applying support vector machine. Biocybern Biomed Eng 39(1):38\u201351","journal-title":"Biocybern Biomed Eng"},{"key":"6481_CR23","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","volume":"128","author":"K Ogurtsova","year":"2017","unstructured":"Ogurtsova K, da Rocha Fernandes J, Huang Y, Linnenkamp U, Guariguata L, Cho NH, Cavan D, Shaw J, Makaroff L (2017) Idf diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract 128:40\u201350","journal-title":"Diabetes Res Clin Pract"},{"issue":"1","key":"6481_CR24","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.eswa.2006.09.012","volume":"34","author":"K Polat","year":"2008","unstructured":"Polat K, G\u00fcne\u015f S, Arslan A (2008) A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Exp Syst Appl 34(1):482\u2013487","journal-title":"Exp Syst Appl"},{"issue":"5","key":"6481_CR25","doi-asserted-by":"publisher","first-page":"2239","DOI":"10.1016\/j.eswa.2013.09.022","volume":"41","author":"M Seera","year":"2014","unstructured":"Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Exp Syst Appl 41(5):2239\u20132249","journal-title":"Exp Syst Appl"},{"key":"6481_CR26","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.compeleceng.2019.03.004","volume":"76","author":"T Shanthi","year":"2019","unstructured":"Shanthi T, Sabeenian R (2019) Modified alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng 76:56\u201364","journal-title":"Comput Electr Eng"},{"issue":"3","key":"6481_CR27","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10462-017-9565-3","volume":"51","author":"BA Tama","year":"2019","unstructured":"Tama BA, Rhee KH (2019) Tree-based classifier ensembles for early detection method of diabetes: an exploratory study. Artif Intell Rev 51(3):355\u2013370","journal-title":"Artif Intell Rev"},{"key":"6481_CR28","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","journal-title":"Inform Med Unlocked"},{"key":"6481_CR29","first-page":"1","volume":"32","author":"JH Wu","year":"2019","unstructured":"Wu JH, Li J, Wang J, Zhang L, Wang HD, Wang GL, Xl Li, Yuan JX (2019) Risk prediction of type 2 diabetes in steel workers based on convolutional neural network. Neural Comput Appl 32:1\u201316","journal-title":"Neural Comput Appl"},{"key":"6481_CR30","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ijleo.2018.11.059","volume":"180","author":"X Yang","year":"2019","unstructured":"Yang X, Fang T, Li Y, Guo L, Li F, Huang F, Li L (2019) Pre-diabetes diagnosis based on atr-ftir spectroscopy combined with cart and xgboots. Optik 180:189\u2013198","journal-title":"Optik"},{"key":"6481_CR31","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 Inf 97:120\u2013127","journal-title":"Int J Med Inf"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06481-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06481-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06481-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T15:37:29Z","timestamp":1642779449000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06481-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6481"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06481-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,13]]},"assertion":[{"value":"11 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2021","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}