{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:43:29Z","timestamp":1781883809935,"version":"3.54.5"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T00:00:00Z","timestamp":1503446400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s40537-017-0082-7","type":"journal-article","created":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T11:13:44Z","timestamp":1503486824000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Survey on clinical prediction models for diabetes prediction"],"prefix":"10.1186","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9756-0463","authenticated-orcid":false,"given":"N.","family":"Jayanthi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B. Vijaya","family":"Babu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. Sambasiva","family":"Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,8,23]]},"reference":[{"key":"82_CR1","volume-title":"Predictive analytics","author":"DE Brown","year":"2015","unstructured":"Brown DE, et al. Predictive analytics. Washington: IEEE Computer Society; 2015."},{"key":"82_CR2","unstructured":"http:\/\/www.predictiveanalyticsworld.com\/patimes\/intro-to-machine-learning-algorithms-for-it-professionals-0620152\/5580\/ . Accessed 2 July 2017."},{"key":"82_CR3","unstructured":"http:\/\/www.who.int\/diabetes\/publications\/en\/screening_mnc03.pdf . Accessed 29 Mar 2017."},{"issue":"2","key":"82_CR4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.14445\/22312803\/IJCTT-V11P120","volume":"11","author":"R Sanakal","year":"2014","unstructured":"Sanakal R, Jayakumari T. Prognosis of diabetes using data mining approach-fuzzy C means clustering and support vector machine. Int J Comput Trends Technol. 2014;11(2):94\u20138.","journal-title":"Int J Comput Trends Technol"},{"issue":"6","key":"82_CR5","first-page":"933","volume":"4","author":"KR Lakshmi","year":"2013","unstructured":"Lakshmi KR, Kumar SP. Utilization of data mining techniques for prediction of diabetes disease survivability. Int J Sci Eng Res. 2013;4(6):933\u201340.","journal-title":"Int J Sci Eng Res"},{"key":"82_CR6","volume-title":"Prediction on diabetes using data mining approach","author":"P Repalli","year":"2011","unstructured":"Repalli P. Prediction on diabetes using data mining approach. Stillwater: Oklahoma State University; 2011."},{"key":"82_CR7","doi-asserted-by":"crossref","unstructured":"Motka R, et al. Diabetes mellitus forecast using different data mining techniques. In: Computer and communication technology (ICCCT), IEEE, 4th international conference. New York: IEEE; 2013.","DOI":"10.1109\/ICCCT.2013.6749610"},{"key":"82_CR8","unstructured":"Eckerson WW. Predictive analytics. Tdwi Research. 2006."},{"key":"82_CR9","unstructured":"http:\/\/data-magnum.com\/types-and-uses-of-predictive-analytics-what-they-are-and-where-you-can-put-them-to-work\/ . Accessed 15 Apr 2017."},{"key":"82_CR10","unstructured":"https:\/\/link.springer.com\/chapter\/10.1057%2F9781137379283_6#page-1 . Accessed 5 July 2017."},{"key":"82_CR11","unstructured":"Kalechofsky H. A simple framework for building predictive models. 2016."},{"key":"82_CR12","unstructured":"Tevet D, et al. Introduction to predictive modeling using GLMs a practitioner\u2019s viewpoint."},{"key":"82_CR13","unstructured":"https:\/\/www.analyticsvidhya.com\/blog\/2015\/08\/comprehensive-guide-regression\/ . Accessed 20 Apr 2017."},{"key":"82_CR14","doi-asserted-by":"crossref","unstructured":"Gemson Andrew Ebenezer J. Big data analytics in healthcare: a survey. ARPN J Eng Appl Sci. 2015;10(8).","DOI":"10.12988\/ces.2015.412255"},{"key":"82_CR15","unstructured":"http:\/\/www.dummies.com\/programming\/big-data\/data-science\/data-science-for-dummies-cheat-sheet\/ . Accessed 30 Mar 2017."},{"key":"82_CR16","unstructured":"Predictive modeling, Julie Chambers, the 56th annual Canadian reinsurance conference."},{"key":"82_CR17","unstructured":"Abbott Analytics. Strategies for building predictive models. 2014."},{"key":"82_CR18","unstructured":"Predictive analytics: poised to drive population health White Paper, Optum."},{"key":"82_CR19","unstructured":"Duncan I. Introduction to predictive modeling. 2015."},{"key":"82_CR20","unstructured":"https:\/\/www.linkedin.com\/pulse\/4-types-predictive-analytics-models-mark-rabkin . Accessed 3 July 2017."},{"key":"82_CR21","unstructured":"] http:\/\/234w.tc.tracom.net\/healthcare\/Pages\/Diabetes-Readmission-Predictive-Analytics.aspx . Accessed 25 Mar 2017."},{"key":"82_CR22","volume-title":"How to establish clinical prediction models","author":"YH Lee","year":"2016","unstructured":"Lee YH, et al. How to establish clinical prediction models. Seoul: Korean Endocrine Society; 2016."},{"issue":"2","key":"82_CR23","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.phrp.2011.07.005","volume":"2","author":"J Lee","year":"2011","unstructured":"Lee J, et al. Development of a predictive model for type 2 diabetes mellitus using genetic and clinical data. Osong Public Health Res Perspect. 2011;2(2):75\u201382.","journal-title":"Osong Public Health Res Perspect"},{"key":"82_CR24","unstructured":"Plis K, et al. A machine learning approach to predicting blood glucose levels for diabetes management. Association for the Advancement of Artificial Intelligence. 2014."},{"key":"82_CR25","doi-asserted-by":"crossref","unstructured":"Yang Y, et.al. Forecasting potential diabetes complications. In: Proceedings of the twenty-eighth AAAI Conference on artificial intelligence, Copyright c. Association for the Advancement of Artificial. 2014.","DOI":"10.1609\/aaai.v28i1.8741"},{"issue":"12","key":"82_CR26","doi-asserted-by":"crossref","first-page":"8102","DOI":"10.1016\/j.eswa.2010.05.078","volume":"37","author":"BM Patil","year":"2010","unstructured":"Patil BM, et al. Hybrid prediction model for type-2 diabetic patients. Expert Syst Appl. 2010;37(12):8102\u20138.","journal-title":"Expert Syst Appl"},{"key":"82_CR27","doi-asserted-by":"crossref","unstructured":"Sarojini Ilango, B. et al. A hybrid prediction model with F-score feature selection for type ii diabetes databases. In: A2CWiC. 2010.","DOI":"10.1145\/1858378.1858391"},{"issue":"4","key":"82_CR28","doi-asserted-by":"crossref","first-page":"8610","DOI":"10.1016\/j.eswa.2008.10.032","volume":"36","author":"H Temurtas","year":"2009","unstructured":"Temurtas H., et al. A comparative study on diabetes disease diagnosis using neural networks. Expert Syst Appl. 2009;36(4):8610\u20135.","journal-title":"Expert Syst Appl"},{"key":"82_CR29","unstructured":"Divya et al. Predictive model for diabetic patients using hybrid twin support vector machine. In: Proc. of int. conf. on advances in communication, network, and computing, CNC. Amsterdam: Elsevier; 2014."},{"issue":"2","key":"82_CR30","first-page":"181","volume":"90","author":"TM Ahmed","year":"2016","unstructured":"Ahmed TM. Developing a predicted model for diabetes type 2 treatment plans by using data mining. J Theor Appl Inf Technol. 2016;90(2):181\u20137.","journal-title":"J Theor Appl Inf Technol"},{"key":"82_CR31","doi-asserted-by":"crossref","unstructured":"Devi MN, et al. Developing a modified logistic regression model for diabetes mellitus and identifying the important factors of type II DM. Indian J Sci Technol. 2016; 9(4).","DOI":"10.17485\/ijst\/2016\/v9i4\/87028"},{"key":"82_CR32","unstructured":"Thirugnanam M, et al. Hybrid tool for diagnosis of diabetes. IIOAB J. 2016;7(5)."},{"key":"82_CR33","doi-asserted-by":"crossref","unstructured":"Osman AH, et al. Diabetes disease diagnosis method based on feature extraction using K-SVM. Int J Adv Comput Sci Appl. 2017;8(1).","DOI":"10.14569\/IJACSA.2017.080130"},{"key":"82_CR34","doi-asserted-by":"crossref","unstructured":"Anand A. Prediction of diabetes based on personal lifestyle indicators. In: 2015 1st international conference on next generation computing technologies (Ngct-2015) Dehradun, India, 4\u20135 September 2015.","DOI":"10.1109\/NGCT.2015.7375206"},{"key":"82_CR35","volume-title":"A computational approach of data smoothening and prediction of diabetes dataset","author":"S Jakhmola","year":"2015","unstructured":"Jakhmola S. A computational approach of data smoothening and prediction of diabetes dataset. New York City: ACM; 2015."},{"key":"82_CR36","doi-asserted-by":"crossref","unstructured":"AlJarullah AA. Decision tree discovery for the diagnosis of type II diabetes. In: International conference on innovations in information technology. New York: IEEE; 2011.","DOI":"10.1109\/INNOVATIONS.2011.5893838"},{"issue":"2","key":"82_CR37","doi-asserted-by":"crossref","first-page":"95","DOI":"10.4258\/hir.2016.22.2.95","volume":"22","author":"Meysam Jahani","year":"2016","unstructured":"Jahani Meysam, Mahdavi Mahdi. Comparison of predictive models for the early diagnosis of diabetes. Healthc Inform Res. 2016;22(2):95\u2013100.","journal-title":"Healthc Inform Res"},{"key":"82_CR38","doi-asserted-by":"crossref","unstructured":"Hashi EK, et al. An expert clinical decision support system to predict disease using classification techniques. In: International conference on electrical, computer and communication engineering (ECCE), \u00a92017 IEEE, February 16\u201318, 2017, Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ECACE.2017.7912937"},{"key":"82_CR39","doi-asserted-by":"crossref","unstructured":"Anand A, Shakti D. Prediction of diabetes based on personal lifestyle indicators. In: Next generation computing technologies (NGCT), 2015 1st international conference on 4\u20135 Sept. New York: IEEE; 2015.","DOI":"10.1109\/NGCT.2015.7375206"},{"key":"82_CR40","doi-asserted-by":"crossref","unstructured":"Zanon M, et al. Regularised model identification improves accuracy of multisensor systems for noninvasive continuous glucose monitoring in diabetes management. J Appl Math. 2013;2013.","DOI":"10.1155\/2013\/793869"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-017-0082-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40537-017-0082-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-017-0082-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T17:22:04Z","timestamp":1659374524000},"score":1,"resource":{"primary":{"URL":"http:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-017-0082-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,23]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["82"],"URL":"https:\/\/doi.org\/10.1186\/s40537-017-0082-7","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,23]]},"article-number":"26"}}