{"id":"https://openalex.org/W4394898257","doi":"https://doi.org/10.1145/3650400.3650563","title":"Exploring Heart Disease Prediction through Machine Learning Techniques","display_name":"Exploring Heart Disease Prediction through Machine Learning Techniques","publication_year":2023,"publication_date":"2023-10-20","ids":{"openalex":"https://openalex.org/W4394898257","doi":"https://doi.org/10.1145/3650400.3650563"},"language":"en","primary_location":{"id":"doi:10.1145/3650400.3650563","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650400.3650563","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering","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/A5010825569","display_name":"Zhicong Lin","orcid":"https://orcid.org/0009-0001-5229-6435"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhicong Lin","raw_affiliation_strings":["School of Computer, Electronics and information, Guangxi University, China"],"raw_orcid":"https://orcid.org/0009-0001-5229-6435","affiliations":[{"raw_affiliation_string":"School of Computer, Electronics and information, Guangxi University, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077529869","display_name":"S. Chen","orcid":"https://orcid.org/0009-0000-5706-920X"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shujing Chen","raw_affiliation_strings":["School of Computer, Electronics and information, Guangxi University, China"],"raw_orcid":"https://orcid.org/0009-0000-5706-920X","affiliations":[{"raw_affiliation_string":"School of Computer, Electronics and information, Guangxi University, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035584689","display_name":"Jichang Chen","orcid":"https://orcid.org/0009-0007-8294-2593"},"institutions":[{"id":"https://openalex.org/I4210151929","display_name":"Nanning Normal University","ror":"https://ror.org/04dx82x73","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210151929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jichang Chen","raw_affiliation_strings":["School of Information Engineering, Nanning University, China"],"raw_orcid":"https://orcid.org/0009-0007-8294-2593","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Nanning University, China","institution_ids":["https://openalex.org/I4210151929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010825569"],"corresponding_institution_ids":["https://openalex.org/I150807315"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40014782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"964","last_page":"969"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12979","display_name":"Cardiovascular Disease and Adiposity","score":0.9929999709129333,"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"}},"topics":[{"id":"https://openalex.org/T12979","display_name":"Cardiovascular Disease and Adiposity","score":0.9929999709129333,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9851999878883362,"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/T13522","display_name":"Cardiovascular Health and Risk Factors","score":0.958899974822998,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.765647292137146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7593085765838623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7259769439697266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7113944888114929},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6609808802604675},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6461569666862488},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.631536602973938},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5998988151550293},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5508256554603577},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.515630841255188},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4823724329471588},{"id":"https://openalex.org/keywords/heart-disease","display_name":"Heart disease","score":0.4728941023349762},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.4553026556968689},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4426882565021515},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4201880693435669},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33756768703460693},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08199015259742737}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.765647292137146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7593085765838623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7259769439697266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7113944888114929},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6609808802604675},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6461569666862488},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.631536602973938},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5998988151550293},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5508256554603577},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.515630841255188},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4823724329471588},{"id":"https://openalex.org/C2780074459","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Heart disease","level":2,"score":0.4728941023349762},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.4553026556968689},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4426882565021515},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4201880693435669},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33756768703460693},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08199015259742737},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3650400.3650563","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650400.3650563","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2096015347","https://openalex.org/W2969762385","https://openalex.org/W3015188018","https://openalex.org/W4210687879","https://openalex.org/W4317952638","https://openalex.org/W4366492109","https://openalex.org/W4366502736","https://openalex.org/W4372258977","https://openalex.org/W4379875317","https://openalex.org/W4383746529","https://openalex.org/W4385059333"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W4366990902","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W4317732970","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W4388550696"],"abstract_inverted_index":{"Currently,":[0],"heart":[1,69,98,114],"disease":[2,99,115],"stands":[3],"as":[4],"the":[5,21,105,108],"most":[6],"formidable":[7],"threat":[8],"to":[9,76],"human":[10],"life.":[11],"The":[12,80],"application":[13,92],"of":[14,23,38,93,107,113],"machine":[15,43],"learning":[16,44],"in":[17],"scrutinizing":[18],"data":[19,66,87],"holds":[20],"promise":[22],"augmenting":[24],"early":[25],"detection":[26],"and":[27,41,56,60,101],"prevention":[28],"strategies":[29],"for":[30,97],"this":[31,34],"ailment.":[32],"Within":[33],"study,":[35,109],"a":[36,77,110],"suite":[37],"six":[39],"distinctive":[40],"classical":[42],"models\u2014Logistic":[45],"Regression,":[46],"Random":[47],"Forest,":[48],"Decision":[49],"Tree,":[50],"K-Nearest":[51],"Neighbor,":[52],"Support":[53],"Vector":[54],"Classifier,":[55],"Neural":[57],"Network\u2014are":[58],"introduced":[59],"meticulously":[61],"evaluated.":[62],"These":[63],"models":[64,96],"leverage":[65],"gathered":[67],"from":[68],"patients":[70],"across":[71],"four":[72],"distinct":[73],"regions,":[74],"contributing":[75],"comprehensive":[78,111],"assessment.":[79],"investigative":[81],"process":[82],"encompasses":[83],"five":[84],"pivotal":[85],"stages:":[86],"collection,":[88],"preprocessing,":[89],"K-Means":[90],"clustering,":[91],"classification":[94],"algorithms":[95],"prediction,":[100],"rigorous":[102],"evaluation.":[103],"At":[104],"end":[106],"summary":[112],"prediction":[116],"was":[117],"presented.":[118]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}