{"id":"https://openalex.org/W3183411741","doi":"https://doi.org/10.1145/3460238.3460245","title":"Identifying Prognostic Features for Predicting Heart Failure by Using Machine Learning Algorithm","display_name":"Identifying Prognostic Features for Predicting Heart Failure by Using Machine Learning Algorithm","publication_year":2021,"publication_date":"2021-03-17","ids":{"openalex":"https://openalex.org/W3183411741","doi":"https://doi.org/10.1145/3460238.3460245","mag":"3183411741"},"language":"en","primary_location":{"id":"doi:10.1145/3460238.3460245","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460238.3460245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 11th International Conference on Biomedical Engineering and Technology","raw_type":"proceedings-article"},"type":"conference-paper","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/A5062642808","display_name":"Md. Al Mehedi Hasan","orcid":"https://orcid.org/0000-0003-2966-7055"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Md Al Mehedi Hasan","raw_affiliation_strings":["The University of Aizu, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Aizu, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005221038","display_name":"Jungpil Shin","orcid":"https://orcid.org/0000-0002-7476-2468"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jungpil Shin","raw_affiliation_strings":["The University of Aizu, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Aizu, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013448273","display_name":"Utsha Das","orcid":null},"institutions":[{"id":"https://openalex.org/I2802576217","display_name":"Green University of Bangladesh","ror":"https://ror.org/04f7afq11","country_code":"BD","type":"education","lineage":["https://openalex.org/I2802576217"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Utsha Das","raw_affiliation_strings":["Green University of Bangladesh, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Green University of Bangladesh, Bangladesh","institution_ids":["https://openalex.org/I2802576217"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076429266","display_name":"Azmain Yakin Srizon","orcid":"https://orcid.org/0000-0003-4674-0365"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azmain Yakin Srizon","raw_affiliation_strings":["Rajshahi University of Engineering&amp;Technology, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rajshahi University of Engineering&amp;Technology, Bangladesh","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.8191,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.93761935,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"40","last_page":"46"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9966999888420105,"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":0.9966999888420105,"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":0.9297999739646912,"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/feature-selection","display_name":"Feature selection","score":0.7556766271591187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7185162901878357},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6897616982460022},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.672094464302063},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6548619270324707},{"id":"https://openalex.org/keywords/heart-failure","display_name":"Heart failure","score":0.6057088375091553},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5310191512107849},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5129682421684265},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.47813212871551514},{"id":"https://openalex.org/keywords/ejection-fraction","display_name":"Ejection fraction","score":0.42935997247695923},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35019850730895996},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34642449021339417},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1466725766658783},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.13845163583755493}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7556766271591187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7185162901878357},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6897616982460022},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.672094464302063},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6548619270324707},{"id":"https://openalex.org/C2778198053","wikidata":"https://www.wikidata.org/wiki/Q181754","display_name":"Heart failure","level":2,"score":0.6057088375091553},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5310191512107849},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5129682421684265},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.47813212871551514},{"id":"https://openalex.org/C78085059","wikidata":"https://www.wikidata.org/wiki/Q641303","display_name":"Ejection fraction","level":3,"score":0.42935997247695923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35019850730895996},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34642449021339417},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1466725766658783},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13845163583755493}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460238.3460245","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460238.3460245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 11th International Conference on Biomedical Engineering and Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W174799281","https://openalex.org/W1175355699","https://openalex.org/W1507701463","https://openalex.org/W1527842677","https://openalex.org/W2005422872","https://openalex.org/W2041406732","https://openalex.org/W2095554003","https://openalex.org/W2105084129","https://openalex.org/W2113665069","https://openalex.org/W2123950519","https://openalex.org/W2127708170","https://openalex.org/W2156145910","https://openalex.org/W2168630917","https://openalex.org/W2409437973","https://openalex.org/W2521419423","https://openalex.org/W2527824850","https://openalex.org/W2553101787","https://openalex.org/W2562498401","https://openalex.org/W2594341951","https://openalex.org/W2614986146","https://openalex.org/W2739315321","https://openalex.org/W2805813844","https://openalex.org/W2806950843","https://openalex.org/W2883464116","https://openalex.org/W2903848813","https://openalex.org/W2920679031","https://openalex.org/W2941884679","https://openalex.org/W2946315093","https://openalex.org/W2946591451","https://openalex.org/W2959146504","https://openalex.org/W2964374934","https://openalex.org/W2968020416","https://openalex.org/W2972732442","https://openalex.org/W3012665221","https://openalex.org/W4210971754"],"related_works":["https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4224922629","https://openalex.org/W4385770464","https://openalex.org/W3125536479","https://openalex.org/W4214820172","https://openalex.org/W3120363735","https://openalex.org/W2394323384"],"abstract_inverted_index":{"Being":[0],"one":[1],"of":[2,125,153,166],"the":[3,38,56,61,73,119,149,156,162,187],"most":[4],"common":[5],"cardiovascular":[6],"diseases,":[7],"heart":[8,25,70,192],"failure":[9,26,193],"caused":[10],"40":[11],"million":[12],"deaths":[13],"worldwide":[14],"in":[15,123],"2015.":[16],"Previously,":[17],"various":[18],"studies":[19],"have":[20],"been":[21],"conducted":[22],"to":[23,46,69],"predict":[24,60],"at":[27,64],"an":[28,65],"early":[29,66],"stage.":[30],"Although":[31],"each":[32],"study":[33],"has":[34],"contributed":[35],"and":[36,77,89,113,132,182],"continued":[37],"development":[39],"process,":[40],"a":[41],"significant":[42],"breakthrough":[43],"is":[44],"still":[45],"be":[47,195],"achieved.":[48],"In":[49],"this":[50],"research,":[51],"we":[52],"focused":[53],"on":[54,94],"finding":[55],"features":[57,139],"which":[58,140,191],"can":[59,173,194],"death":[62],"probability":[63],"stage":[67],"due":[68],"failure.":[71],"Firstly,":[72],"dataset":[74],"was":[75,121],"acquired":[76],"preprocessed.":[78],"After":[79],"that,":[80],"two":[81,138,188],"feature":[82,91,145],"selection":[83,146],"approaches,":[84],"minimum":[85],"redundancy":[86],"maximum":[87],"relevance,":[88],"recursive":[90],"elimination":[92],"based":[93],"Na\u00efve":[95],"Bayes":[96],"were":[97,116,141],"employed.":[98],"Then,":[99],"five":[100],"machine":[101],"learning":[102],"classifiers,":[103],"support":[104],"vector":[105],"machine,":[106],"logistic":[107],"regression,":[108],"decision":[109,157],"tree,":[110],"na\u00efve":[111],"bayes":[112],"k-nearest":[114],"neighbors,":[115],"utilized.":[117],"Finally,":[118],"performance":[120],"measured":[122],"terms":[124],"accuracy,":[126],"sensitivity,":[127],"specificity,":[128],"f1-score,":[129],"MCC":[130],"value":[131],"AUC":[133],"value.":[134],"It":[135],"turned":[136],"out":[137],"selected":[142],"by":[143,190],"both":[144],"techniques,":[147],"achieved":[148],"highest":[150],"overall":[151],"accuracy":[152],"80%":[154],"for":[155],"tree":[158],"classifier.":[159],"Comparison":[160],"with":[161,175],"previous":[163],"best":[164],"result":[165],"58.5%":[167],"proved":[168],"that":[169,179],"our":[170],"proposed":[171],"methodology":[172],"comment":[174],"much":[176],"more":[177],"certainty":[178],"Ejection":[180],"Fraction":[181],"Serum":[183],"Creatinine":[184],"are":[185],"indeed":[186],"factors":[189],"predicted.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
