{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:23:18Z","timestamp":1773922998985,"version":"3.50.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007160","name":"President\u2019s Convergence Science Initiative Grant from Wichita State University","doi-asserted-by":"publisher","award":["U29001"],"award-info":[{"award-number":["U29001"]}],"id":[{"id":"10.13039\/100007160","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3131128","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T20:26:49Z","timestamp":1637872009000},"page":"157337-157360","source":"Crossref","is-referenced-by-count":30,"title":["A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3419-980X","authenticated-orcid":false,"given":"Hera","family":"Siddiqui","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1541-8202","authenticated-orcid":false,"given":"Ajita","family":"Rattani","sequence":"additional","affiliation":[]},{"given":"Nikki K.","family":"Woods","sequence":"additional","affiliation":[]},{"given":"Laila","family":"Cure","sequence":"additional","affiliation":[]},{"given":"Rhonda K.","family":"Lewis","sequence":"additional","affiliation":[]},{"given":"Janet","family":"Twomey","sequence":"additional","affiliation":[]},{"given":"Betty","family":"Smith-Campbell","sequence":"additional","affiliation":[]},{"given":"Twyla J.","family":"Hill","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2017.00014"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1186\/s12966-015-0175-7"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59861-7_12"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16234684"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/DESSERT.2019.8770016"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-019-00101-y"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.12.011"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.earlhumdev.2010.07.006"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5341-7_109"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpeds.2021.02.010"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1080\/15374416.2016.1157758"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1136\/bmjnph-2020-000074"},{"key":"ref28","article-title":"Obesity prediction with EHR data: A deep learning approach with interpretable elements","author":"gupta","year":"2019","journal-title":"arXiv 1912 02655"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2017.8217988"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0215571"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.320.7244.1240"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcn\/70.1.126s"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1089\/chi.2020.0324"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.12.003"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1049\/iet-bmt.2012.0019"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"506","DOI":"10.4338\/ACI-2015-03-RA-0036","article-title":"Machine learning techniques for prediction of early childhood obesity","volume":"6","author":"dugan","year":"2015","journal-title":"Appl Clin Informat"},{"key":"ref23","first-page":"438","article-title":"Computational intelligence approach for modeling hydrogen production: A review","volume":"12","author":"ardabili","year":"2018","journal-title":"Eng App of Comp Fluid Mech"},{"key":"ref26","first-page":"465","article-title":"Data mining techniques for classification of childhood obesity among year 6 school children","author":"abdullah","year":"2016","journal-title":"Proc Int Conf Soft Comput Data Mining"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4338\/ACI-2016-01-RA-0015"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3945\/ajcn.2009.27113C"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1126\/science.1124779"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.copsyc.2015.09.005"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/S1526-9523(03)00281-2"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.acap.2021.04.031"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104392"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.102985"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2019.07.003"},{"key":"ref53","article-title":"AI-based BMI inference from facial images: An application to weight monitoring","author":"siddiqui","year":"2020","journal-title":"arXiv 2010 07442"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IIAI-AAI.2014.175"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthplace.2015.08.002"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-81205-8"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2018.04.022"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.pedn.2019.02.004"},{"key":"ref14","first-page":"1","article-title":"A survey on utilization of data mining for childhood obesity prediction","author":"adnan","year":"2010","journal-title":"Proc 8th Asia&#x2013;Pacific Symp Inf Telecommun Technol"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ANNES.1995.499512"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CBMS.1996.507129"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-009-9157-0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101844"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/nu12082466"},{"key":"ref4","author":"kuczmarski","year":"2002","journal-title":"2000 CDC Growth Charts for United States Methods and Development"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2471\/BLT.07.043497"},{"key":"ref6","first-page":"281","article-title":"A hybrid approach using na&#x00EF;ve Bayes and genetic algorithm for childhood obesity prediction","volume":"1","author":"adnan","year":"2012","journal-title":"Proc Int Conf Comput Inf Sci (ICCIS)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291719001545"},{"key":"ref8","first-page":"75","article-title":"Parameter identification and selection for childhood obesity prediction using data mining","volume":"35","author":"adnan","year":"2012","journal-title":"Proc 2nd Int Conf Manage Artif Intell"},{"key":"ref7","first-page":"99","article-title":"Hybrid approaches using decision tree, naive bayes, means and Euclidean distances for childhood obesity prediction","volume":"6","author":"adnan","year":"2012","journal-title":"Int J Softw Eng Appl"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1590\/S1020-49892013000500006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3109\/17477166.2010.545410"},{"key":"ref46","first-page":"1","article-title":"Face-to-BMI: Using computer vision to infer body mass index on social media","volume":"11","author":"kocabey","year":"2017","journal-title":"Proc Int AAAI Conf Web Social Media"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2018.1535"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1017\/S1368980009991789"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3109\/17477160903268282"},{"key":"ref42","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00235"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcn.2017.10.002"},{"key":"ref43","article-title":"Hawthorne effect","author":"spencer","year":"2017","journal-title":"Catalogue Of Bias"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09627712.pdf?arnumber=9627712","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T22:20:23Z","timestamp":1643667623000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9627712\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3131128","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}