{"id":"https://openalex.org/W4389543058","doi":"https://doi.org/10.1109/embc40787.2023.10340229","title":"Machine learning-based classification and risk factor analysis of frailty in Korean community-dwelling older adults","display_name":"Machine learning-based classification and risk factor analysis of frailty in Korean community-dwelling older adults","publication_year":2023,"publication_date":"2023-07-24","ids":{"openalex":"https://openalex.org/W4389543058","doi":"https://doi.org/10.1109/embc40787.2023.10340229","pmid":"https://pubmed.ncbi.nlm.nih.gov/38082748"},"language":"en","primary_location":{"id":"doi:10.1109/embc40787.2023.10340229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc40787.2023.10340229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 45th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-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/A5043788673","display_name":"Heeeun Jung","orcid":"https://orcid.org/0000-0002-5821-7873"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]},{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA","KR"],"is_corresponding":true,"raw_author_name":"Heeeun Jung","raw_affiliation_strings":["KIST,Center for Artificial Intelligence,Seoul,South Korea","KHU-KIST Department of Converging Science, Technology, Graduate School, Kyung Hee University, Seoul, South Korea","Center for Artificial Intelligence, KIST, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"KIST,Center for Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I4210164862"]},{"raw_affiliation_string":"KHU-KIST Department of Converging Science, Technology, Graduate School, Kyung Hee University, Seoul, South Korea","institution_ids":["https://openalex.org/I35928602"]},{"raw_affiliation_string":"Center for Artificial Intelligence, KIST, Seoul, South Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041870047","display_name":"Miji Kim","orcid":"https://orcid.org/0000-0002-0852-8825"},"institutions":[{"id":"https://openalex.org/I4210160630","display_name":"Kyung Hee University East-West Neo Medical Center","ror":"https://ror.org/053d97e59","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I4210160630"]},{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Miji Kim","raw_affiliation_strings":["Kyung Hee University,College of Medicine, East-West Medical Research Institute,Department of Biomedical Science and Technology,Seoul,South Korea","Department of Biomedical Science and Technology, College of Medicine, East-West Medical Research Institute, Kyung Hee University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Kyung Hee University,College of Medicine, East-West Medical Research Institute,Department of Biomedical Science and Technology,Seoul,South Korea","institution_ids":["https://openalex.org/I4210160630","https://openalex.org/I35928602"]},{"raw_affiliation_string":"Department of Biomedical Science and Technology, College of Medicine, East-West Medical Research Institute, Kyung Hee University, Seoul, South Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087469762","display_name":"Chang Won Won","orcid":"https://orcid.org/0000-0002-6429-4461"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chang Won Won","raw_affiliation_strings":["College of Medicine, Kyung Hee University,Elderly Frailty Research Center,Department of Family Medicine,Seoul,South Korea","Department of Family Medicine, Elderly Frailty Research Center, College of Medicine, Kyung Hee University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"College of Medicine, Kyung Hee University,Elderly Frailty Research Center,Department of Family Medicine,Seoul,South Korea","institution_ids":["https://openalex.org/I35928602"]},{"raw_affiliation_string":"Department of Family Medicine, Elderly Frailty Research Center, College of Medicine, Kyung Hee University, Seoul, South Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100753952","display_name":"Jin\u2010Wook Kim","orcid":"https://orcid.org/0000-0003-0934-3344"},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jinwook Kim","raw_affiliation_strings":["KIST,Center for Artificial Intelligence,Seoul,South Korea","Center for Artificial Intelligence, KIST, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"KIST,Center for Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I4210164862"]},{"raw_affiliation_string":"Center for Artificial Intelligence, KIST, Seoul, South Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050659837","display_name":"Kyung-Ryoul Mun","orcid":"https://orcid.org/0000-0002-5482-1117"},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kyung-Ryoul Mun","raw_affiliation_strings":["KIST,Center for Artificial Intelligence,Seoul,South Korea","Center for Artificial Intelligence, KIST, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"KIST,Center for Artificial Intelligence,Seoul,South Korea","institution_ids":["https://openalex.org/I4210164862"]},{"raw_affiliation_string":"Center for Artificial Intelligence, KIST, Seoul, South Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043788673"],"corresponding_institution_ids":["https://openalex.org/I35928602","https://openalex.org/I4210164862"],"apc_list":null,"apc_paid":null,"fwci":0.7352,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.71212809,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"2023","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11011","display_name":"Frailty in Older Adults","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2717","display_name":"Geriatrics and Gerontology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11011","display_name":"Frailty in Older Adults","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2717","display_name":"Geriatrics and Gerontology"},"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/T10397","display_name":"Nutrition and Health in Aging","score":0.9661999940872192,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/T10804","display_name":"Health Systems, Economic Evaluations, Quality of Life","score":0.9413999915122986,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8389540314674377},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7182235717773438},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7037469148635864},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6845186352729797},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6828530430793762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6472529768943787},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5395359992980957},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5308662056922913},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5023162364959717},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.32032549381256104}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8389540314674377},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7182235717773438},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7037469148635864},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6845186352729797},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6828530430793762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6472529768943787},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5395359992980957},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5308662056922913},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5023162364959717},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.32032549381256104}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000073496","descriptor_name":"Frailty","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D000073496","descriptor_name":"Frailty","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D000073496","descriptor_name":"Frailty","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D000073496","descriptor_name":"Frailty","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D000073496","descriptor_name":"Frailty","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"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":"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":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","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":"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":"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":"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":"D056910","descriptor_name":"Republic of Korea","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D056910","descriptor_name":"Republic of Korea","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D056910","descriptor_name":"Republic of Korea","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D056910","descriptor_name":"Republic of Korea","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D056910","descriptor_name":"Republic of Korea","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D057187","descriptor_name":"Independent Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057187","descriptor_name":"Independent Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057187","descriptor_name":"Independent Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057187","descriptor_name":"Independent Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057187","descriptor_name":"Independent Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc40787.2023.10340229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc40787.2023.10340229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 45th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:38082748","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38082748","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":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311419","display_name":"Ministry of Health","ror":null},{"id":"https://openalex.org/F4320322014","display_name":"Ministry of Food and Drug Safety","ror":"https://ror.org/01f7dp456"},{"id":"https://openalex.org/F4320322091","display_name":"Korea Institute of Science and Technology","ror":"https://ror.org/05kzfa883"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2022181538","https://openalex.org/W2164059021","https://openalex.org/W2919200815","https://openalex.org/W3004855998","https://openalex.org/W3019128236","https://openalex.org/W3094992426","https://openalex.org/W3200669805","https://openalex.org/W4290565942"],"related_works":["https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W4367336074","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W3154045278","https://openalex.org/W4379620016","https://openalex.org/W4393666307","https://openalex.org/W3210764983","https://openalex.org/W4393443811"],"abstract_inverted_index":{"Frailty":[0],"is":[1,22],"a":[2,68],"dynamic":[3],"reversible":[4],"state,":[5],"characterized":[6],"by":[7,170],"frequent":[8],"transitions":[9],"between":[10],"frailty":[11,21,39,146,157,175],"status":[12,147],"over":[13],"time.":[14],"The":[15,111,133],"timely":[16],"and":[17,41,54,93,101,128,148,164,168],"effective":[18,142],"detection":[19],"of":[20,49,79,126,131],"important":[23],"to":[24,32,42,106],"prevent":[25,167],"adverse":[26],"health":[27],"outcomes.":[28],"This":[29],"study":[30],"aims":[31],"develop":[33],"machine":[34,137],"learning-based":[35],"classification":[36,109],"models":[37],"for":[38,76,144],"assessment":[40],"investigate":[43],"its":[44],"risk":[45,63],"factors.":[46],"A":[47],"total":[48],"1,482":[50],"subjects,":[51],"1,266":[52],"robust":[53],"216":[55],"frail":[56,62],"older":[57],"adults,":[58],"were":[59,65,104],"analyzed.":[60],"Sixteen":[61],"factors":[64],"selected":[66,117],"from":[67],"random":[69,94],"forest-based":[70],"feature":[71],"selection":[72],"method,":[73],"then":[74],"used":[75],"the":[77,108,116,120],"inputs":[78],"five":[80],"ML":[81],"models:":[82],"logistic":[83,112],"regression,":[84],"K-nearest":[85],"neighbor,":[86],"support":[87],"vector":[88],"machine,":[89],"gaussian":[90],"na\u00efve":[91],"bayes,":[92],"forest.":[95],"Data":[96],"resampling,":[97],"stratified":[98],"10-fold":[99],"cross-validation,":[100],"grid":[102],"search":[103],"applied":[105],"improve":[107,169],"performance.":[110],"regression":[113],"model":[114],"using":[115,158],"features":[118],"showed":[119],"best":[121],"performance":[122],"with":[123],"an":[124,129,141],"accuracy":[125],"0.93":[127],"F<sub>1</sub>-score":[130],"0.92.":[132],"results":[134],"suggest":[135],"that":[136,173],"learning":[138],"techniques":[139],"are":[140],"method":[143],"classifying":[145],"exploring":[149],"frailty-related":[150],"factors.Clinical":[151],"Relevance-":[152],"Our":[153],"approach":[154],"can":[155,165],"predict":[156],"data":[159],"collectable":[160],"in":[161],"clinical":[162],"setting":[163],"help":[166],"identifying":[171],"variables":[172],"change":[174],"status.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
