{"id":"https://openalex.org/W2889179825","doi":"https://doi.org/10.1109/iwcmc.2018.8450376","title":"Sample expansion-oriented LDBN heart disease risk forecast model","display_name":"Sample expansion-oriented LDBN heart disease risk forecast model","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2889179825","doi":"https://doi.org/10.1109/iwcmc.2018.8450376","mag":"2889179825"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc.2018.8450376","doi":"https://doi.org/10.1109/iwcmc.2018.8450376","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc.2018.8450376","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"International Wireless Communications and Mobile Computing Conference (IWCMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Wireless Communications &amp; Mobile Computing Conference (IWCMC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5102775589","display_name":"Zhenfei Wang","orcid":"https://orcid.org/0000-0003-4188-5231","observed_orcids":["https://orcid.org/0000-0003-4188-5231"]},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenfei Wang","raw_affiliation_strings":["College of Information Engineering, Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015260276","display_name":"Jinlei Chen","orcid":"https://orcid.org/0000-0003-3775-3586","observed_orcids":["https://orcid.org/0000-0003-3775-3586","https://orcid.org/0009-0002-2814-1406"]},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlei Chen","raw_affiliation_strings":["College of Information Engineering, Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100558588","display_name":"Zhiyun Zheng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyun Zheng","raw_affiliation_strings":["College of Information Engineering, Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101481396","display_name":"Junfeng Wang","orcid":"https://orcid.org/0000-0002-4701-4237","observed_orcids":["https://orcid.org/0000-0002-4701-4237"]},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Wang","raw_affiliation_strings":["College of Information Engineering, Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I38877650"],"apc_list":null,"apc_paid":null,"fwci":0.7238,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65451997,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9990000128746033,"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":0.9990000128746033,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T10320","display_name":"Neural Networks and Applications","score":9.999999747378752e-05,"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/heart-disease-risk-prediction","display_name":"heart disease risk prediction","score":0.9739999771118164},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"long short-term memory","score":0.8809999823570251},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"deep belief network","score":0.8220000267028809},{"id":"https://openalex.org/keywords/sample-expansion","display_name":"sample expansion","score":0.6779999732971191},{"id":"https://openalex.org/keywords/medical-data-acquisition","display_name":"medical data acquisition","score":0.6470000147819519},{"id":"https://openalex.org/keywords/small-sample-learning","display_name":"small sample learning","score":0.5590000152587891},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.6690000295639038}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693609952926636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6228922009468079},{"id":"https://openalex.org/C2780074459","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Heart disease","level":2,"score":0.5756394863128662},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5575825572013855},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.505699634552002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49999332427978516},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.49852991104125977},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4273304343223572},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4151947498321533},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35394880175590515},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.27292218804359436},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.14082103967666626},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13439369201660156},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc.2018.8450376","doi":"https://doi.org/10.1109/iwcmc.2018.8450376","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc.2018.8450376","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"International Wireless Communications and Mobile Computing Conference (IWCMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Wireless Communications &amp; Mobile Computing Conference (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2005792051","https://openalex.org/W2102970634","https://openalex.org/W2116064496","https://openalex.org/W2611214388","https://openalex.org/W2766352633","https://openalex.org/W2791514042"],"related_works":["https://openalex.org/W7117670632","https://openalex.org/W7217640885","https://openalex.org/W7133195859","https://openalex.org/W3162745688","https://openalex.org/W7217266524","https://openalex.org/W7204490448","https://openalex.org/W3007513993","https://openalex.org/W4396721150","https://openalex.org/W2800253831","https://openalex.org/W2970585395"],"abstract_inverted_index":{"The":[0],"risk":[1,27,63,132],"forecast":[2,28,64,74,133],"model":[3,107,134,187],"of":[4,15,39,61,77,101,111,167,173,184],"heart":[5,16,25,45,114,126],"disease":[6,26,46,115],"provides":[7],"an":[8],"important":[9],"method":[10],"for":[11,142],"predicting":[12],"the":[13,34,37,50,58,75,78,90,96,106,109,118,153,171,174,182,185],"incidence":[14],"disease.":[17,127],"This":[18],"paper":[19],"proposes":[20],"a":[21,131],"sample":[22],"expansion-oriented":[23],"LDBN":[24,137],"model,":[29,181],"in":[30,44,57],"order":[31],"to":[32,121,169],"solve":[33],"following":[35],"problems:":[36],"difficulty":[38],"related":[40],"medical":[41,123],"data":[42,124],"acquisition":[43],"diagnosis,":[47],"less":[48],"samples,":[49],"slow":[51],"convergence":[52],"speeds":[53],"and":[54,87,116,152,162,179],"easily":[55],"caught":[56],"local":[59],"optimum":[60],"existing":[62,85],"models.":[65],"Starting":[66],"from":[67,84],"LSTM":[68],"(Long":[69,138,158],"Short":[70,159],"Term":[71,160],"Memory)":[72,161],"that":[73],"information":[76,86,94,100,110],"next":[79],"moment":[80,92],"with":[81,89,113,147,177],"extract":[82],"knowledge":[83],"combined":[88],"current":[91],"input":[93],"or":[95],"previous":[97],"layer":[98],"output":[99],"DNN":[102],"(Deep":[103,149],"Neural":[104],"Networks),":[105],"simulates":[108],"patients":[112],"adopts":[117],"corresponding":[119],"strategies":[120],"expand":[122],"about":[125],"Then,":[128],"we":[129],"build":[130],"based":[135],"on":[136],"Deep":[139],"Belief":[140,150],"Networks)":[141],"more":[143],"accurate":[144],"prediction":[145],"combing":[146],"DBN":[148,180],"Network)":[151],"improved":[154],"recurrent":[155],"neural":[156],"Network":[157],"use":[163],"Heart":[164],"Disease":[165],"Databases":[166],"UCI":[168],"verify":[170],"accuracy":[172,183],"model.":[175],"Comparing":[176],"BP":[178],"proposed":[186],"is":[188],"respectively":[189],"increased":[190],"by":[191],"2.9.":[192]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2018-09-07T00:00:00"}
