{"id":"https://openalex.org/W2772778802","doi":"https://doi.org/10.1109/bibm.2017.8217987","title":"A novel hybrid subset-learning method for predicting risk factors of atherosclerosis","display_name":"A novel hybrid subset-learning method for predicting risk factors of atherosclerosis","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2772778802","doi":"https://doi.org/10.1109/bibm.2017.8217987","mag":"2772778802"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2017.8217987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5100677765","display_name":"Jiang Xie","orcid":"https://orcid.org/0000-0001-9944-8822"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiang Xie","raw_affiliation_strings":["Shanghai Institute for Advanced Communication and Data Science, Shanghai University, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457206","display_name":"Haitao Wang","orcid":"https://orcid.org/0000-0001-6910-974X"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Wang","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100691291","display_name":"Jiyuan Zhang","orcid":"https://orcid.org/0000-0003-1360-237X"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyuan Zhang","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047585822","display_name":"Chao Meng","orcid":"https://orcid.org/0009-0003-7155-2101"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Meng","raw_affiliation_strings":["Department of Geriatrics, Shanghai Jiao-Tong University Medical School, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Geriatrics, Shanghai Jiao-Tong University Medical School, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068853308","display_name":"Yanyan Kong","orcid":"https://orcid.org/0000-0001-7274-5683"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Kong","raw_affiliation_strings":["PET Center, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"PET Center, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033011079","display_name":"Shanping Mao","orcid":"https://orcid.org/0000-0003-3438-5650"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanping Mao","raw_affiliation_strings":["Department of Neurology, Hospital of Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Department of Neurology, Hospital of Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101471667","display_name":"Lingyu Xu","orcid":"https://orcid.org/0000-0003-2992-3733"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyu Xu","raw_affiliation_strings":["Shanghai Institute for Advanced Communication and Data Science, Shanghai University, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111753179","display_name":"Wu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Zhang","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100677765"],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":null,"apc_paid":null,"fwci":1.445,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8724393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2124","last_page":"2131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9819999933242798,"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":0.9819999933242798,"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/T12955","display_name":"Atherosclerosis and Cardiovascular Diseases","score":0.9240000247955322,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7276975512504578},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6538770794868469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6452105045318604},{"id":"https://openalex.org/keywords/neglect","display_name":"Neglect","score":0.5353413820266724},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.5305004119873047},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4542454183101654},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4130604565143585},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.264616996049881},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.14362382888793945}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7276975512504578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6538770794868469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6452105045318604},{"id":"https://openalex.org/C2776289891","wikidata":"https://www.wikidata.org/wiki/Q1931511","display_name":"Neglect","level":2,"score":0.5353413820266724},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.5305004119873047},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4542454183101654},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4130604565143585},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.264616996049881},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.14362382888793945},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2017.8217987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1515206644","https://openalex.org/W1520348292","https://openalex.org/W1956620150","https://openalex.org/W1966518039","https://openalex.org/W1981283808","https://openalex.org/W2003733635","https://openalex.org/W2052605150","https://openalex.org/W2063859300","https://openalex.org/W2066091004","https://openalex.org/W2085770564","https://openalex.org/W2095649738","https://openalex.org/W2125283600","https://openalex.org/W2125377929","https://openalex.org/W2127427378","https://openalex.org/W2132029215","https://openalex.org/W2133211142","https://openalex.org/W2203714058","https://openalex.org/W2224103056","https://openalex.org/W2262759681","https://openalex.org/W2290121903","https://openalex.org/W2314720829","https://openalex.org/W2401599758","https://openalex.org/W2406182434","https://openalex.org/W2509730946","https://openalex.org/W2603008931","https://openalex.org/W2614693897","https://openalex.org/W3098871113","https://openalex.org/W4236137412","https://openalex.org/W6687554754","https://openalex.org/W6735486338"],"related_works":["https://openalex.org/W2159755425","https://openalex.org/W3213683101","https://openalex.org/W3035095237","https://openalex.org/W3186233728","https://openalex.org/W4200459988","https://openalex.org/W4361733776","https://openalex.org/W4281776617","https://openalex.org/W3020897463","https://openalex.org/W4319005243","https://openalex.org/W4398756457"],"abstract_inverted_index":{"Cardiovascular":[0],"disease":[1,24],"(CVD)":[2],"caused":[3],"by":[4],"atherosclerosis":[5],"is":[6],"one":[7],"of":[8,12,30,38,75,93,133,140],"the":[9,36,39,43,72,84,91,101,116,131,138,145],"major":[10],"causes":[11],"death":[13],"world-wide.":[14],"Currently,":[15],"diverse":[16],"machine":[17,61,135],"learning":[18,62,136],"models":[19],"have":[20,87],"been":[21],"applied":[22],"to":[23,33,68,130,143],"prediction":[25],"and":[26,41,70,108,137],"classification.":[27],"However,":[28],"most":[29],"them":[31],"tend":[32],"focus":[34],"on":[35,90,100],"performance":[37,122],"algorithm":[40,81],"neglect":[42],"underlying":[44],"variables":[45,85],"for":[46],"patients":[47],"in":[48],"different":[49,77],"carotid":[50,94],"atherosclerotic":[51],"stages.":[52,78],"In":[53],"this":[54],"paper,":[55],"we":[56],"propose":[57],"a":[58],"novel":[59],"hybrid":[60],"method":[63,118],"named":[64],"Subset":[65],"Learning":[66],"(S-learning)":[67],"predict":[69],"discover":[71],"risk":[73,141],"factors":[74,142],"these":[76],"The":[79,112],"S-learning":[80],"can":[82],"elucidate":[83],"that":[86,115],"significant":[88],"influence":[89],"outcome":[92],"atherosclerotic.":[95],"Performance":[96],"comparisons":[97],"are":[98],"based":[99],"dataset":[102],"collected":[103],"from":[104],"both":[105],"Shanghai":[106,109],"Renji":[107],"Huashan":[110],"Hospital.":[111],"result":[113],"shows":[114],"proposed":[117],"has":[119],"superior":[120],"classification":[121,125],"than":[123],"other":[124],"algorithms.":[126],"Our":[127],"findings":[128],"point":[129],"utility":[132],"predictive":[134],"discovery":[139],"refine":[144],"treatment":[146],"plans.":[147]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}