{"id":"https://openalex.org/W2014057135","doi":"https://doi.org/10.1109/jbhi.2015.2396520","title":"Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning","display_name":"Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning","publication_year":2015,"publication_date":"2015-02-06","ids":{"openalex":"https://openalex.org/W2014057135","doi":"https://doi.org/10.1109/jbhi.2015.2396520","mag":"2014057135","pmid":"https://pubmed.ncbi.nlm.nih.gov/25675467"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2015.2396520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2015.2396520","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076324160","display_name":"Bum Ju Lee","orcid":"https://orcid.org/0000-0003-2682-5716"},"institutions":[{"id":"https://openalex.org/I4210087584","display_name":"Korea Institute of Oriental Medicine","ror":"https://ror.org/005rpmt10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210087584","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Bum Ju Lee","raw_affiliation_strings":["Medical Research Division, Korea Institute of Oriental Medicine, Daejeon, Korea"],"affiliations":[{"raw_affiliation_string":"Medical Research Division, Korea Institute of Oriental Medicine, Daejeon, Korea","institution_ids":["https://openalex.org/I4210087584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078305799","display_name":"Jong Yeol Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087584","display_name":"Korea Institute of Oriental Medicine","ror":"https://ror.org/005rpmt10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210087584","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jong Yeol Kim","raw_affiliation_strings":["Medical Research Division, Korea Institute of Oriental Medicine, Daejeon, Korea"],"affiliations":[{"raw_affiliation_string":"Medical Research Division, Korea Institute of Oriental Medicine, Daejeon, Korea","institution_ids":["https://openalex.org/I4210087584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076324160"],"corresponding_institution_ids":["https://openalex.org/I4210087584"],"apc_list":null,"apc_paid":null,"fwci":12.3588,"has_fulltext":false,"cited_by_count":130,"citation_normalized_percentile":{"value":0.9840778,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"20","issue":"1","first_page":"39","last_page":"46"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9914000034332275,"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":0.9914000034332275,"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":0.9799000024795532,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6801528930664062},{"id":"https://openalex.org/keywords/anthropometry","display_name":"Anthropometry","score":0.6511395573616028},{"id":"https://openalex.org/keywords/type-2-diabetes","display_name":"Type 2 diabetes","score":0.6302658319473267},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5469248294830322},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5134811997413635},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.4977688789367676},{"id":"https://openalex.org/keywords/phenotype","display_name":"Phenotype","score":0.4455293118953705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3643445670604706},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.35544949769973755},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.29406747221946716},{"id":"https://openalex.org/keywords/endocrinology","display_name":"Endocrinology","score":0.18089082837104797},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.17027881741523743},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.16545438766479492},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.15034407377243042}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6801528930664062},{"id":"https://openalex.org/C61427482","wikidata":"https://www.wikidata.org/wiki/Q6656244","display_name":"Anthropometry","level":2,"score":0.6511395573616028},{"id":"https://openalex.org/C2777180221","wikidata":"https://www.wikidata.org/wiki/Q3025883","display_name":"Type 2 diabetes","level":3,"score":0.6302658319473267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5469248294830322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5134811997413635},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.4977688789367676},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.4455293118953705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3643445670604706},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.35544949769973755},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.29406747221946716},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.18089082837104797},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.17027881741523743},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.16545438766479492},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.15034407377243042},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000886","descriptor_name":"Anthropometry","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000886","descriptor_name":"Anthropometry","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000886","descriptor_name":"Anthropometry","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003430","descriptor_name":"Cross-Sectional Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003430","descriptor_name":"Cross-Sectional Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003430","descriptor_name":"Cross-Sectional Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000097","qualifier_name":"blood","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000097","qualifier_name":"blood","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000097","qualifier_name":"blood","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000503","qualifier_name":"physiopathology","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000503","qualifier_name":"physiopathology","is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000503","qualifier_name":"physiopathology","is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010641","descriptor_name":"Phenotype","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010641","descriptor_name":"Phenotype","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010641","descriptor_name":"Phenotype","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014280","descriptor_name":"Triglycerides","qualifier_ui":"Q000097","qualifier_name":"blood","is_major_topic":false},{"descriptor_ui":"D014280","descriptor_name":"Triglycerides","qualifier_ui":"Q000097","qualifier_name":"blood","is_major_topic":false},{"descriptor_ui":"D014280","descriptor_name":"Triglycerides","qualifier_ui":"Q000097","qualifier_name":"blood","is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2015.2396520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2015.2396520","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:25675467","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/25675467","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G5069588349","display_name":null,"funder_award_id":"NRF-2012-0009830","funder_id":"https://openalex.org/F4320322030","funder_display_name":"Ministry of Science, ICT and Future Planning"},{"id":"https://openalex.org/G5839066048","display_name":null,"funder_award_id":"NRF-2009-0090900","funder_id":"https://openalex.org/F4320322030","funder_display_name":"Ministry of Science, ICT and Future Planning"},{"id":"https://openalex.org/G6697314063","display_name":null,"funder_award_id":"2006-2005173","funder_id":"https://openalex.org/F4320322030","funder_display_name":"Ministry of Science, ICT and Future Planning"}],"funders":[{"id":"https://openalex.org/F4320322030","display_name":"Ministry of Science, ICT and Future Planning","ror":"https://ror.org/032e49973"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W349770100","https://openalex.org/W1574715084","https://openalex.org/W1943214615","https://openalex.org/W1965895350","https://openalex.org/W1969929577","https://openalex.org/W1973794180","https://openalex.org/W1976807345","https://openalex.org/W1977651272","https://openalex.org/W1986096805","https://openalex.org/W1991251155","https://openalex.org/W1991610154","https://openalex.org/W1999043764","https://openalex.org/W1999318832","https://openalex.org/W2004315330","https://openalex.org/W2012374504","https://openalex.org/W2019028576","https://openalex.org/W2019086545","https://openalex.org/W2022632655","https://openalex.org/W2023865680","https://openalex.org/W2028444308","https://openalex.org/W2029815565","https://openalex.org/W2030353283","https://openalex.org/W2031070957","https://openalex.org/W2033016720","https://openalex.org/W2036313311","https://openalex.org/W2040480279","https://openalex.org/W2049694972","https://openalex.org/W2049873535","https://openalex.org/W2051436059","https://openalex.org/W2054910713","https://openalex.org/W2058606479","https://openalex.org/W2067329062","https://openalex.org/W2067583750","https://openalex.org/W2068758891","https://openalex.org/W2082057981","https://openalex.org/W2083451552","https://openalex.org/W2088032151","https://openalex.org/W2090678102","https://openalex.org/W2093337137","https://openalex.org/W2109674748","https://openalex.org/W2114853550","https://openalex.org/W2116679817","https://openalex.org/W2118978333","https://openalex.org/W2131008509","https://openalex.org/W2133990480","https://openalex.org/W2134293572","https://openalex.org/W2141224284","https://openalex.org/W2147572724","https://openalex.org/W2154706222","https://openalex.org/W2158460187","https://openalex.org/W2162777527","https://openalex.org/W2163098384","https://openalex.org/W2163614729","https://openalex.org/W2169095714","https://openalex.org/W2427874386","https://openalex.org/W2553319248","https://openalex.org/W4285719527","https://openalex.org/W4385822927","https://openalex.org/W6611762666","https://openalex.org/W6684034121","https://openalex.org/W6730088551"],"related_works":["https://openalex.org/W3120995422","https://openalex.org/W2159447326","https://openalex.org/W2997659300","https://openalex.org/W2497800675","https://openalex.org/W3114984321","https://openalex.org/W4299869453","https://openalex.org/W267534745","https://openalex.org/W2119761735","https://openalex.org/W4396904540","https://openalex.org/W1593064325"],"abstract_inverted_index":{"The":[0,33,235],"hypertriglyceridemic":[1],"waist":[2,204],"(HW)":[3],"phenotype":[4,47],"is":[5],"strongly":[6,172],"associated":[7,173],"with":[8,116,174,301],"type":[9,49,117,175,221,232,255,302,327,349],"2":[10,50,118,176,222,233,256,303,328,350],"diabetes;":[11],"however,":[12],"to":[13,40,56,106,142,238],"date,":[14],"no":[15],"study":[16,38],"has":[17],"assessed":[18],"the":[19,36,42,45,58,144,164,166,213,216,227,249,251,293,298,305,309,313,322,337,345],"predictive":[20,59,145,241,306],"power":[21,60,146,242,307],"of":[22,35,61,65,67,147,163,168,212,254,295,308,312,325,339,348],"phenotypes":[23,63,236],"based":[24],"on":[25],"individual":[26,68,123],"anthropometric":[27,69,98,124],"measurements":[28,70,311],"and":[29,48,55,71,77,93,96,114,122,138,207,220,231,275,316],"triglyceride":[30],"(TG)":[31],"levels.":[32,73],"aims":[34],"present":[37],"were":[39,140,153,258],"assess":[41],"association":[43,217,228,300],"between":[44,111,218,229],"HW":[46,121,169,214,296],"diabetes":[51,119,177,223,257],"in":[52,83,190,200,243,246,263,280],"Korean":[53],"adults":[54],"evaluate":[57,143],"various":[62,148],"consisting":[64],"combinations":[66],"TG":[72,94,208,230,262,279,317],"Between":[74],"November":[75],"2006":[76],"August":[78],"2013,":[79],"11,937":[80],"subjects":[81,113],"participated":[82],"this":[84],"retrospective":[85],"cross-sectional":[86],"study.":[87],"We":[88,100],"measured":[89],"fasting":[90],"plasma":[91],"glucose":[92],"levels":[95,209],"performed":[97,154],"measurements.":[99,125],"employed":[101],"binary":[102],"logistic":[103],"regression":[104],"(LR)":[105],"examine":[107],"statistically":[108],"significant":[109],"differences":[110],"normal":[112],"those":[115],"using":[120,155],"For":[126],"more":[127],"reliable":[128],"prediction":[129,151],"results,":[130],"two":[131],"machine":[132],"learning":[133],"algorithms,":[134],"naive":[135],"Bayes":[136],"(NB)":[137],"LR,":[139],"used":[141],"phenotypes.":[149],"All":[150],"experiments":[152],"a":[156],"tenfold":[157],"cross":[158],"validation":[159],"method.":[160],"Among":[161,248],"all":[162],"variables,":[165],"presence":[167,294],"was":[170,224],"most":[171],"(p":[178],"<":[179,193],"0.001,":[180,194],"adjusted":[181,195],"odds":[182],"ratio":[183,260,277],"(OR)":[184],"=":[185,197,268,273,285,290],"2.07":[186],"[95%":[187],"CI,":[188],"1.72-2.49]":[189],"men;":[191],"p":[192],"OR":[196],"2.09":[198],"[1.79-2.45]":[199],"women).":[201],"When":[202],"comparing":[203],"circumference":[205],"(WC)":[206],"as":[210],"components":[211],"phenotype,":[215],"WC":[219,315],"greater":[225],"than":[226,245],"diabetes.":[234,329,351],"tended":[237],"have":[239],"higher":[240],"women":[244,281],"men.":[247],"phenotypes,":[250],"best":[252,323],"predictors":[253],"waist-to-hip":[259],"+":[261,278],"men":[264],"(AUC":[265,282],"by":[266,271,283,288],"NB":[267,284],"0.653,":[269],"AUC":[270,287],"LR":[272,289],"0.661)":[274],"rib-to-hip":[276],"0.73,":[286],"0.735).":[291],"Although":[292],"demonstrated":[297],"strongest":[299],"diabetes,":[304],"combined":[310],"actual":[314],"values":[318],"may":[319,332],"not":[320],"be":[321],"manner":[324],"predicting":[326],"Our":[330],"findings":[331],"provide":[333],"clinical":[334,340],"information":[335],"concerning":[336],"development":[338],"decision":[341],"support":[342],"systems":[343],"for":[344],"initial":[346],"screening":[347]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
