{"id":"https://openalex.org/W4283268545","doi":"https://doi.org/10.1145/3529836.3529863","title":"A Machine Learning-Based Approach for Cardiovascular Diseases Prediction","display_name":"A Machine Learning-Based Approach for Cardiovascular Diseases Prediction","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4283268545","doi":"https://doi.org/10.1145/3529836.3529863"},"language":"en","primary_location":{"id":"doi:10.1145/3529836.3529863","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529863","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","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/A5091814512","display_name":"Haoran Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haoran Lyu","raw_affiliation_strings":["Department of Statistics and Applied Probability/University of California, Santa Barbara, University of California, Santa Barbara, United States"],"affiliations":[{"raw_affiliation_string":"Department of Statistics and Applied Probability/University of California, Santa Barbara, University of California, Santa Barbara, United States","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5091814512"],"corresponding_institution_ids":["https://openalex.org/I154570441"],"apc_list":null,"apc_paid":null,"fwci":0.4972,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75779755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"59","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9925000071525574,"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":0.9925000071525574,"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/T10144","display_name":"Blood Pressure and Hypertension Studies","score":0.9898999929428101,"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/T11620","display_name":"Medication Adherence and Compliance","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2714","display_name":"Family Practice"},"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/machine-learning","display_name":"Machine learning","score":0.703229546546936},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6832642555236816},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6470044851303101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6374889016151428},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6300789713859558},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6249127388000488},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6221289038658142},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.5916464924812317},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5225322246551514},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.5188985466957092},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4544743001461029},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.44949954748153687},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42315512895584106},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.39460939168930054},{"id":"https://openalex.org/keywords/risk-analysis","display_name":"Risk analysis (engineering)","score":0.35569554567337036},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32564496994018555},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2127278447151184}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.703229546546936},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6832642555236816},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6470044851303101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6374889016151428},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6300789713859558},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6249127388000488},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6221289038658142},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.5916464924812317},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5225322246551514},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.5188985466957092},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4544743001461029},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.44949954748153687},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42315512895584106},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39460939168930054},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.35569554567337036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32564496994018555},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2127278447151184},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529836.3529863","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529863","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","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":12,"referenced_works":["https://openalex.org/W1519043595","https://openalex.org/W1678356000","https://openalex.org/W1901616594","https://openalex.org/W2006653131","https://openalex.org/W2031968478","https://openalex.org/W2049895654","https://openalex.org/W2125283600","https://openalex.org/W2163207768","https://openalex.org/W2338452106","https://openalex.org/W2605253636","https://openalex.org/W2735762027","https://openalex.org/W4246988009"],"related_works":["https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W2004826645","https://openalex.org/W4366990902","https://openalex.org/W4388550696","https://openalex.org/W4224922629","https://openalex.org/W4321636153","https://openalex.org/W4313289487","https://openalex.org/W3191198889"],"abstract_inverted_index":{"In":[0],"recent":[1],"decades,":[2],"the":[3,8,31,51,61,69,87,91,95,111,123,149,160,178],"development":[4],"of":[5,10,33,53,68,97,110,122,151],"technology":[6],"and":[7,30,63,72,101,184],"increase":[9],"living":[11],"standards":[12],"have":[13],"affected":[14],"people's":[15],"attention":[16,27],"in":[17,41,80,90,116,159],"healthcare.":[18],"The":[19,174],"healthcare":[20,43,168],"market":[21],"has":[22,36,107,188],"expanded":[23],"along":[24],"with":[25,131,139,155,170],"great":[26],"from":[28],"researchers,":[29],"study":[32,148,165],"disease":[34,46,54,118,204],"forecasting":[35],"become":[37],"an":[38,199],"inevitable":[39],"process":[40],"future":[42],"development.":[44],"Cardiovascular":[45],"is":[47],"a":[48,57],"category":[49],"for":[50,142,202],"type":[52],"that":[55],"causes":[56],"negative":[58],"impact":[59],"on":[60,128],"heart":[62],"blood":[64],"vessels.":[65],"As":[66],"one":[67,109],"most":[70,112,121],"common":[71],"lethal":[73],"potential":[74,88],"diseases,":[75],"predicting":[76],"cardiovascular":[77,117,203],"diseases":[78],"helped":[79],"high-risk":[81],"patients'":[82],"decision-making":[83],"process,":[84],"which":[85,195],"identifies":[86],"threats":[89],"early":[92],"stages.":[93],"With":[94],"help":[96,197],"tremendous":[98],"medical":[99],"data":[100,169],"increased":[102],"computational":[103],"capabilities,":[104],"machine":[105],"learning":[106],"proved":[108],"efficient":[113],"prediction":[114],"methods":[115],"forecasting.":[119],"However,":[120],"proposed":[124],"research":[125],"either":[126],"focuses":[127],"single":[129,140],"algorithms":[130,138,154],"different":[132],"parameter":[133],"settings":[134],"or":[135],"large-scale":[136],"selected":[137,153,193],"criteria":[141,157],"modeling.":[143],"This":[144],"experiment":[145,175],"aims":[146],"to":[147],"performance":[150,190],"small-scale":[152],"multiple":[156],"used":[158,166],"modeling":[161],"process.":[162],"Specifically,":[163],"this":[164],"historical":[167],"fourteen":[171],"attributes":[172],"selected.":[173],"results":[176],"show":[177],"Random":[179],"Forest":[180],"built":[181],"by":[182],"Classification":[183],"Regression":[185],"Trees":[186],"(CART)":[187],"dominant":[189],"among":[191],"all":[192],"algorithms,":[194],"can":[196],"construct":[198],"alert":[200],"system":[201],"prediction.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}