{"id":"https://openalex.org/W7225629383","doi":"https://doi.org/10.3390/medsci14070766","title":"Sex-Specific Explainable Machine Learning Models of Diabetic-Range 120-Minute OGTT Glucose Using Non-Invasive Variables","display_name":"Sex-Specific Explainable Machine Learning Models of Diabetic-Range 120-Minute OGTT Glucose Using Non-Invasive Variables","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225629383","doi":"https://doi.org/10.3390/medsci14070766"},"language":"en","primary_location":{"id":"doi:10.3390/medsci14070766","doi":"https://doi.org/10.3390/medsci14070766","is_oa":true,"landing_page_url":"https://doi.org/10.3390/medsci14070766","pdf_url":null,"source":{"id":"https://openalex.org/S2739445098","display_name":"Medical Sciences","issn_l":"2076-3271","issn":["2076-3271"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","medline","norway-1"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/medsci14070766","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137740772","display_name":"Yongjun Kim","orcid":"https://orcid.org/0009-0008-6239-2085","observed_orcids":["https://orcid.org/0009-0008-6239-2085"]},"institutions":[{"id":"https://openalex.org/I21028721","display_name":"Eulji University","ror":"https://ror.org/005bty106","country_code":"KR","type":"education","lineage":["https://openalex.org/I21028721"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"YongJun Kim","raw_affiliation_strings":["Department of Big Data Medical Convergence, Eulji University, 553 Sanseong-daero, Seongnam-si 13135, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Big Data Medical Convergence, Eulji University, 553 Sanseong-daero, Seongnam-si 13135, Republic of Korea","institution_ids":["https://openalex.org/I21028721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003669409","display_name":"JooYong Park","orcid":"https://orcid.org/0000-0002-6444-3754","observed_orcids":["https://orcid.org/0000-0002-6444-3754"]},"institutions":[{"id":"https://openalex.org/I21028721","display_name":"Eulji University","ror":"https://ror.org/005bty106","country_code":"KR","type":"education","lineage":["https://openalex.org/I21028721"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"JooYong Park","raw_affiliation_strings":["Department of Big Data Medical Convergence, Eulji University, 553 Sanseong-daero, Seongnam-si 13135, Republic of Korea","Department of Big Data and Artificial Intelligence, Eulji University, 553 Sanseong-daero, Seongnam-si 13135, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-6444-3754","affiliations":[{"raw_affiliation_string":"Department of Big Data Medical Convergence, Eulji University, 553 Sanseong-daero, Seongnam-si 13135, Republic of Korea","institution_ids":["https://openalex.org/I21028721"]},{"raw_affiliation_string":"Department of Big Data and Artificial Intelligence, Eulji University, 553 Sanseong-daero, Seongnam-si 13135, Republic of Korea","institution_ids":["https://openalex.org/I21028721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5003669409"],"corresponding_institution_ids":["https://openalex.org/I21028721"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1689},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1689},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":"7","first_page":"766","last_page":"766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9535999894142151,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9535999894142151,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10027","display_name":"Diabetes, Cardiovascular Risks, and Lipoproteins","score":0.022600000724196434,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.008299999870359898,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.6330000162124634},{"id":"https://openalex.org/keywords/body-fat","display_name":"fat mass","score":0.8050000071525574},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"blood pressure","score":0.8619999885559082},{"id":"https://openalex.org/keywords/age","display_name":"age","score":0.609000027179718},{"id":"https://openalex.org/keywords/korean-genome-and-epidemiology-study","display_name":"Korean Genome and Epidemiology Study","score":0.7429999709129333},{"id":"https://openalex.org/keywords/diabetes-risk-prediction","display_name":"diabetes risk prediction","score":0.503000020980835}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.8863999843597412},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6826000213623047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5957000255584717},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.48429998755455017},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C107130276","wikidata":"https://www.wikidata.org/wiki/Q133805","display_name":"Epidemiology","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C2776294035","wikidata":"https://www.wikidata.org/wiki/Q3480702","display_name":"Lactate threshold","level":5,"score":0.2953999936580658},{"id":"https://openalex.org/C201903717","wikidata":"https://www.wikidata.org/wiki/Q1778788","display_name":"Cohort study","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C2780221984","wikidata":"https://www.wikidata.org/wiki/Q131191","display_name":"Body mass index","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/medsci14070766","doi":"https://doi.org/10.3390/medsci14070766","is_oa":true,"landing_page_url":"https://doi.org/10.3390/medsci14070766","pdf_url":null,"source":{"id":"https://openalex.org/S2739445098","display_name":"Medical Sciences","issn_l":"2076-3271","issn":["2076-3271"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","medline","norway-1"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/medsci14070766","doi":"https://doi.org/10.3390/medsci14070766","is_oa":true,"landing_page_url":"https://doi.org/10.3390/medsci14070766","pdf_url":null,"source":{"id":"https://openalex.org/S2739445098","display_name":"Medical Sciences","issn_l":"2076-3271","issn":["2076-3271"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","medline","norway-1"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.9922999739646912,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1966716734","https://openalex.org/W2038216402","https://openalex.org/W2118978333","https://openalex.org/W2185264818","https://openalex.org/W2295598076","https://openalex.org/W2341084900","https://openalex.org/W2345328813","https://openalex.org/W2792883159","https://openalex.org/W2887976033","https://openalex.org/W2911964244","https://openalex.org/W2913997948","https://openalex.org/W2949676527","https://openalex.org/W2991033511","https://openalex.org/W2999615587","https://openalex.org/W4205805845","https://openalex.org/W4229984599","https://openalex.org/W4315434406","https://openalex.org/W4400681085","https://openalex.org/W4409747009","https://openalex.org/W4409868667","https://openalex.org/W4417092938","https://openalex.org/W4417121491"],"related_works":["https://openalex.org/W4409747009","https://openalex.org/W2792883159","https://openalex.org/W2896927272","https://openalex.org/W4386629161","https://openalex.org/W7123362712","https://openalex.org/W4295872850","https://openalex.org/W7214344852","https://openalex.org/W4391648217","https://openalex.org/W7164906337","https://openalex.org/W7160771311"],"abstract_inverted_index":{"Background/Objectives:":[0],"This":[1],"study":[2],"evaluated":[3,89],"whether":[4],"sex-specific":[5],"machine":[6],"learning":[7],"models":[8,69,176],"using":[9,72],"non-invasive":[10,74,193],"variables":[11,158],"could":[12],"identify":[13],"individuals":[14],"with":[15,76,181],"glucose":[16,27,206],"concentrations":[17],"of":[18,161],"\u2265200":[19],"mg/dL":[20],"at":[21],"120":[22],"min":[23],"during":[24],"an":[25],"oral":[26],"tolerance":[28],"test":[29,92],"(OGTT)":[30],"and":[31,47,54,59,81,84,94,121,138,149,165],"examined":[32],"the":[33,51,108,155,162],"characteristics":[34,194],"contributing":[35,195],"to":[36,117,124,196,218],"model":[37,102],"predictions.":[38],"Methods:":[39],"Baseline":[40],"data":[41],"from":[42,115,122],"8555":[43],"participants":[44],"(4055":[45],"men":[46,120,137,148],"4500":[48],"women)":[49],"in":[50,119,126,136,142,147,151,159,171],"Korean":[52],"Genome":[53],"Epidemiology":[55],"Study":[56],"(KoGES)":[57],"Ansan":[58],"Ansung":[60],"cohort":[61],"were":[62,70,99,132],"analyzed":[63],"separately":[64],"by":[65],"sex.":[66],"Five":[67],"classification":[68],"developed":[71],"20":[73],"variables,":[75],"class":[77],"weighting":[78],"where":[79],"supported":[80],"F2-based":[82],"hyperparameter":[83],"threshold":[85],"optimization.":[86],"Performance":[87],"was":[88,168],"on":[90],"held-out":[91],"sets,":[93],"SHapley":[95],"Additive":[96],"exPlanations":[97],"(SHAP)":[98],"used":[100],"for":[101,187],"interpretation.":[103],"Results:":[104],"The":[105,128,175],"area":[106],"under":[107],"receiver":[109],"operating":[110],"characteristic":[111],"curve":[112],"(AUROC)":[113],"ranged":[114],"0.579":[116],"0.665":[118],"0.656":[123],"0.735":[125],"women.":[127,143],"highest":[129],"F2":[130],"scores":[131],"0.276":[133],"(logistic":[134],"regression)":[135],"0.300":[139],"(random":[140],"forest)":[141],"Body":[144],"fat":[145],"mass":[146],"age":[150],"women":[152],"ranked":[153],"among":[154],"top":[156],"three":[157],"four":[160],"five":[163],"models,":[164],"blood":[166],"pressure":[167],"frequently":[169],"important":[170],"both":[172],"sexes.":[173],"Conclusions:":[174],"showed":[177],"modest":[178],"predictive":[179],"performance,":[180],"low":[182],"precision":[183],"limiting":[184],"their":[185],"use":[186],"OGTT":[188],"selection.":[189],"Model":[190],"explanations":[191,210],"identified":[192],"positive":[197],"predictions":[198],"that":[199],"may":[200],"inform":[201],"clinical":[202,212],"risk-factor":[203],"review":[204],"alongside":[205],"testing.":[207],"Whether":[208],"these":[209],"improve":[211],"decisions":[213],"or":[214],"patient":[215],"outcomes":[216],"remains":[217],"be":[219],"established.":[220]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
