{"id":"https://openalex.org/W4318147994","doi":"https://doi.org/10.1109/bigdata55660.2022.10020522","title":"Comparing Association Rules and Deep Neural Networks for Heart Disease Prediction","display_name":"Comparing Association Rules and Deep Neural Networks for Heart Disease Prediction","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147994","doi":"https://doi.org/10.1109/bigdata55660.2022.10020522"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020522","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020522","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-04714416","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031610238","display_name":"Carlos Ordo\u0144\u1ebdz","orcid":"https://orcid.org/0009-0005-1135-9726"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Carlos Ordonez","raw_affiliation_strings":["University of Houston,Department of Computer Science,USA","Department of computer Science [Houston] (3122 Duncan Hall, 6100 Main Street, Houston TX 77005 - United States)","University of Houston (Houston, TX 77204 - United States)"],"affiliations":[{"raw_affiliation_string":"University of Houston,Department of Computer Science,USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Department of computer Science [Houston] (3122 Duncan Hall, 6100 Main Street, Houston TX 77005 - United States)","institution_ids":[]},{"raw_affiliation_string":"University of Houston (Houston, TX 77204 - United States)","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063526715","display_name":"Ian Fund","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ian Fund","raw_affiliation_strings":["University of Houston,Department of Computer Science,USA","Department of computer Science [Houston] (3122 Duncan Hall, 6100 Main Street, Houston TX 77005 - United States)","University of Houston (Houston, TX 77204 - United States)"],"affiliations":[{"raw_affiliation_string":"University of Houston,Department of Computer Science,USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Department of computer Science [Houston] (3122 Duncan Hall, 6100 Main Street, Houston TX 77005 - United States)","institution_ids":[]},{"raw_affiliation_string":"University of Houston (Houston, TX 77204 - United States)","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006507514","display_name":"Ladjel Bellatreche","orcid":"https://orcid.org/0000-0001-9968-0066"},"institutions":[{"id":"https://openalex.org/I171246369","display_name":"\u00c9cole Nationale Sup\u00e9rieure de M\u00e9canique et d'A\u00e9rotechnique","ror":"https://ror.org/04jx68594","country_code":"FR","type":"facility","lineage":["https://openalex.org/I171246369"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ladjel Bellatreche","raw_affiliation_strings":["ENSMA,LIAS/ISAE,France"],"affiliations":[{"raw_affiliation_string":"ENSMA,LIAS/ISAE,France","institution_ids":["https://openalex.org/I171246369"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031610238"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":0.1465,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46818247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"13","issue":null,"first_page":"4416","last_page":"4424"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9951000213623047,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/computer-science","display_name":"Computer science","score":0.7755584716796875},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5940796732902527},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5897662043571472},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.5782853960990906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5539150834083557},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5536898374557495},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5459808111190796},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5392948389053345},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.5119277238845825},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.493475079536438},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.42178812623023987}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7755584716796875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5940796732902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5897662043571472},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.5782853960990906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5539150834083557},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5536898374557495},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5459808111190796},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5392948389053345},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.5119277238845825},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.493475079536438},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.42178812623023987},{"id":"https://openalex.org/C188082640","wikidata":"https://www.wikidata.org/wiki/Q1780899","display_name":"Complementation","level":4,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020522","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020522","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04714416v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04714416","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data), Dec 2022, Osaka, Japan. pp.4416-4424, ⟨10.1109/BigData55660.2022.10020522⟩","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04714416v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04714416","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data), Dec 2022, Osaka, Japan. pp.4416-4424, ⟨10.1109/BigData55660.2022.10020522⟩","raw_type":"Conference papers"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W259338706","https://openalex.org/W1538006201","https://openalex.org/W1541793614","https://openalex.org/W1822090013","https://openalex.org/W1911464264","https://openalex.org/W1977482345","https://openalex.org/W2055086715","https://openalex.org/W2095705004","https://openalex.org/W2097998348","https://openalex.org/W2117911348","https://openalex.org/W2118781674","https://openalex.org/W2124611117","https://openalex.org/W2216888608","https://openalex.org/W2311599690","https://openalex.org/W2560674852","https://openalex.org/W2788853733","https://openalex.org/W2903557836","https://openalex.org/W2911419447","https://openalex.org/W3006889321","https://openalex.org/W3139392673","https://openalex.org/W4205606665","https://openalex.org/W4251402925","https://openalex.org/W6607142230","https://openalex.org/W6632147796","https://openalex.org/W6637242042","https://openalex.org/W6674330103","https://openalex.org/W6674385629","https://openalex.org/W6684859321","https://openalex.org/W6748587240"],"related_works":["https://openalex.org/W2751920613","https://openalex.org/W2415164632","https://openalex.org/W2238349241","https://openalex.org/W2355668701","https://openalex.org/W2370453500","https://openalex.org/W1561334777","https://openalex.org/W3012205960","https://openalex.org/W2079402849","https://openalex.org/W2359717995","https://openalex.org/W2338045944"],"abstract_inverted_index":{"Two":[0],"decades":[1],"ago,":[2],"the":[3,19,28,111,134],"most":[4,20],"popular":[5,21],"data":[6,32,155,179],"mining":[7],"technique":[8],"were":[9],"association":[10,137,190],"rules":[11,138,159],"(ARs).":[12],"Nowadays":[13],"deep":[14],"neural":[15],"networks":[16],"(DNNs)":[17],"are":[18,43,185],"mechanism":[22],"for":[23,45],"building":[24],"predictive":[25,46,162],"models.":[26],"On":[27],"other":[29],"hand,":[30],"medical":[31],"sets,":[33],"despite":[34],"being":[35],"generally":[36],"small":[37],"in":[38],"size":[39],"(low":[40],"volume),":[41],"they":[42,165],"challenging":[44],"models":[47,74],"due":[48],"to":[49,71,75,92,99],"diverse":[50],"attribute":[51],"content":[52],"(high":[53,60],"variety)":[54],"and":[55,89,133,164,181],"variables":[56],"with":[57,123],"low":[58,146],"redundancy":[59],"variability).":[61],"In":[62],"this":[63],"work":[64],"we":[65],"compare":[66],"these":[67],"two":[68,116],"analytic":[69],"techniques":[70,84,197],"identify":[72],"effective":[73],"predict":[76],"heart":[77],"disease,":[78],"a":[79,101,127,153,167],"multi-target":[80],"prediction":[81],"problem.":[82],"Both":[83],"require":[85],"expertise,":[86],"manual":[87],"tuning,":[88],"iterative":[90],"experimentation":[91],"determine":[93],"optimal":[94],"parameters.":[95],"Our":[96],"goal":[97],"is":[98,105],"build":[100],"DNN":[102],"model":[103],"that":[104],"at":[106,145],"least":[107],"as":[108,110],"good":[109],"best":[112],"ARs.":[113],"There":[114],"exist":[115],"Big":[117],"Data":[118],"challenges:":[119],"risks":[120],"factors":[121],"combined":[122],"imaging":[124],"attributes":[125],"produce":[126],"large":[128],"number":[129,135],"of":[130,136,174],"hidden":[131],"patterns":[132],"reaches":[139],"millions,":[140],"without":[141],"using":[142],"search":[143],"constraints":[144],"support":[147],"(frequency)":[148],"values.":[149],"Preliminary":[150],"experiments":[151],"on":[152],"real":[154],"set":[156],"show":[157],"discovered":[158],"have":[160],"high":[161],"accuracy":[163],"provide":[166],"highly":[168,171],"accurate,":[169],"but":[170,192],"specific,":[172],"profile":[173],"sick":[175],"patients.":[176],"Despite":[177],"careful":[178],"pre-processing":[180],"hyper-parameter":[182],"tuning":[183],"DNNs":[184],"slightly":[186],"more":[187,193],"accurate":[188],"than":[189],"rules,":[191],"generalizable.":[194],"Therefore,":[195],"both":[196],"can":[198],"complement":[199],"each":[200],"other.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}