{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T23:16:15Z","timestamp":1782170175801,"version":"3.54.5"},"reference-count":41,"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\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100011093","name":"University of Phayao","doi-asserted-by":"publisher","award":["FF64-UoE004"],"award-info":[{"award-number":["FF64-UoE004"]}],"id":[{"id":"10.13039\/501100011093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004154","name":"Mae Fah Luang University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004154","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.2021.3117269","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T04:25:24Z","timestamp":1633407924000},"page":"137015-137028","source":"Crossref","is-referenced-by-count":46,"title":["Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3297-4198","authenticated-orcid":false,"given":"Pratya","family":"Nuankaew","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Supansa","family":"Chaising","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9847-157X","authenticated-orcid":false,"given":"Punnarumol","family":"Temdee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","year":"2020","journal-title":"How Did They Determine Diagnostic Thresholds?"},{"key":"ref38","author":"rashid","year":"2020","journal-title":"Diabetes Data"},{"key":"ref33","first-page":"48","article-title":"Information gain feature selection for multi-label classification","volume":"6","author":"pereira","year":"2015","journal-title":"J Inf Data Manage"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0166017"},{"key":"ref31","first-page":"625","article-title":"A novel feature selection based on information gain using WordNet","author":"patil","year":"2013","journal-title":"Proc Sci Inf Conf"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-019-06639-w"},{"key":"ref37","year":"2020","journal-title":"Pima Indians Diabetes Database"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3084623"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5120\/9321-3554"},{"key":"ref34","first-page":"18","article-title":"Feature selection based on information gain","volume":"2","author":"azhagusundari","year":"2013","journal-title":"Int J Innov Technol Exploring Eng"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106441"},{"key":"ref40","author":"indrayan","year":"2020","journal-title":"Diagnostic Thresholds of Medical Measurements"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005540"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2945129"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICICOS.2018.8621683"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICAICTA.2014.7005947"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3047942"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3349\/ymj.2019.60.2.191"},{"key":"ref17","first-page":"36","article-title":"Diagnosis of diabetes mellitus using K nearest neighbor algorithm","volume":"2","author":"saxena","year":"2014","journal-title":"Int J Comput Sci Trends Technol"},{"key":"ref18","first-page":"9","article-title":"Diabetes prediction using machine learning techniques","volume":"8","author":"joshi","year":"2018","journal-title":"Journal of Applied Engineering Research"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.336"},{"key":"ref28","article-title":"Weighted Chebyshev distance classification method for hyperspectral imaging","volume":"9482","author":"demirci","year":"2015","journal-title":"Proc SPIE"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1152\/ajpendo.00124.2020"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/su11030871"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2337\/dc09-9032"},{"key":"ref6","year":"2020","journal-title":"Diabetes Detection Initiative Finding the Undiagnosed"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/GWS.2018.8686527"},{"key":"ref5","year":"2019","journal-title":"Global Diabetes Data Report 2010–2045"},{"key":"ref8","year":"2020","journal-title":"International Diabetes Federation - Facts & figures"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.4103\/2468-8827.184853"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2765639"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s40265-014-0306-5"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2039485"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-020-03719-8"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.08.021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2018.00515"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.01.016"},{"key":"ref41","author":"brownlee","year":"2020","journal-title":"A Gentle Introduction to K-Fold Cross-Validation Mach Learn Mastery (2018)"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3319532"},{"key":"ref26","first-page":"1","article-title":"Comparison between Euclidean and Manhattan distance measure for facial expressions classification","author":"latifa","year":"2017","journal-title":"Proc Int Conf Wireless Technol Embedded Intell Syst (WITS)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCAF.2017.8344721"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09557282.pdf?arnumber=9557282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:50Z","timestamp":1639770950000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9557282\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3117269","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}