An Imbalanced-Data Processing Algorithm for the Prediction of Heart Attack in Stroke Patients

Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. How to predict heart attack...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE access Ročník 9; s. 25394 - 25404
Hlavní autoři:	Wang, Meng, Yao, Xinghua, Chen, Yixiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Cardiac arrest Classifiers Clustering Clustering algorithms Data analysis Data processing Feature extraction Heart heart attack Heart attacks imbalanced data Intensive care Machine learning Oversampling Patients Performance prediction Prediction algorithms Predictive models Stroke Stroke (medical condition) Undersampling
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. How to predict heart attack in the stroke-patient data becomes a challenge. For processing the imbalanced data, this paper designs an algorithm by leveraging random undersampling, clustering and oversampling techniques, which is called undersampling-clustering-oversampling algorithm (shortly, UCO algorithm). The UCO algorithm generates nearly balanced data which are utilized to train machine-learning models for predicting heart attack. Over the database of Medical Information Mart for Intensive Care III, extensive experiments are conducted to evaluate the UCO algorithm. A setting of undersampling number of 120 in the algorithm UCO, denoted UCO(120), shows good performance in helping machine-learning classifiers extract features. Five classifiers are separately deployed to predict heart attack based on outputs of the UCO(120). Our results show that random forest classifier achieves the best predicting performance with an <inline-formula> <tex-math notation="LaTeX">accuracy </tex-math></inline-formula> of 70.29%, and <inline-formula> <tex-math notation="LaTeX">precision </tex-math></inline-formula> of 70.05%. It could be well-predicted using UCO(120) and random forest that whether a stroke patient will have heart attack or not.
AbstractList	Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. How to predict heart attack in the stroke-patient data becomes a challenge. For processing the imbalanced data, this paper designs an algorithm by leveraging random undersampling, clustering and oversampling techniques, which is called undersampling-clustering-oversampling algorithm (shortly, UCO algorithm). The UCO algorithm generates nearly balanced data which are utilized to train machine-learning models for predicting heart attack. Over the database of Medical Information Mart for Intensive Care III, extensive experiments are conducted to evaluate the UCO algorithm. A setting of undersampling number of 120 in the algorithm UCO, denoted UCO(120), shows good performance in helping machine-learning classifiers extract features. Five classifiers are separately deployed to predict heart attack based on outputs of the UCO(120). Our results show that random forest classifier achieves the best predicting performance with an <inline-formula> <tex-math notation="LaTeX">accuracy </tex-math></inline-formula> of 70.29%, and <inline-formula> <tex-math notation="LaTeX">precision </tex-math></inline-formula> of 70.05%. It could be well-predicted using UCO(120) and random forest that whether a stroke patient will have heart attack or not. Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. How to predict heart attack in the stroke-patient data becomes a challenge. For processing the imbalanced data, this paper designs an algorithm by leveraging random undersampling, clustering and oversampling techniques, which is called undersampling-clustering-oversampling algorithm (shortly, UCO algorithm). The UCO algorithm generates nearly balanced data which are utilized to train machine-learning models for predicting heart attack. Over the database of Medical Information Mart for Intensive Care III, extensive experiments are conducted to evaluate the UCO algorithm. A setting of undersampling number of 120 in the algorithm UCO, denoted UCO(120), shows good performance in helping machine-learning classifiers extract features. Five classifiers are separately deployed to predict heart attack based on outputs of the UCO(120). Our results show that random forest classifier achieves the best predicting performance with an accuracy of 70.29%, and precision of 70.05%. It could be well-predicted using UCO(120) and random forest that whether a stroke patient will have heart attack or not. Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. How to predict heart attack in the stroke-patient data becomes a challenge. For processing the imbalanced data, this paper designs an algorithm by leveraging random undersampling, clustering and oversampling techniques, which is called undersampling-clustering-oversampling algorithm (shortly, UCO algorithm). The UCO algorithm generates nearly balanced data which are utilized to train machine-learning models for predicting heart attack. Over the database of Medical Information Mart for Intensive Care III, extensive experiments are conducted to evaluate the UCO algorithm. A setting of undersampling number of 120 in the algorithm UCO, denoted UCO(120), shows good performance in helping machine-learning classifiers extract features. Five classifiers are separately deployed to predict heart attack based on outputs of the UCO(120). Our results show that random forest classifier achieves the best predicting performance with an [Formula Omitted] of 70.29%, and [Formula Omitted] of 70.05%. It could be well-predicted using UCO(120) and random forest that whether a stroke patient will have heart attack or not.
Author	Chen, Yixiang Yao, Xinghua Wang, Meng
Author_xml	– sequence: 1 givenname: Meng orcidid: 0000-0001-8959-1983 surname: Wang fullname: Wang, Meng organization: East China Normal University, Shanghai, China – sequence: 2 givenname: Xinghua orcidid: 0000-0002-2852-0847 surname: Yao fullname: Yao, Xinghua email: xhyao@shutcm.edu.cn organization: School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China – sequence: 3 givenname: Yixiang surname: Chen fullname: Chen, Yixiang organization: East China Normal University, Shanghai, China
BookMark	eNqFUcFq3DAQFSWFpJt8QS6Cnr2VLEu2jmabNguBFjY9BjGWxxttvFIqKYf-fbV1CKWXzmWGmfdmHvM-kDMfPBJyzdmac6Y_9ZvNzW63rlnN14LJVmnxjlzUXOlKSKHO_qrPyVVKB1aiKy3ZXpCH3tPtcYAZvMWx-gwZ6PcYLKbk_J728z5Elx-PdAqR5kcsQxydzS54GiZ6ixAz7XMG-0Sdp7scw1MBQXboc7ok7yeYE1695hX58eXmfnNb3X37ut30d5VtWJcrFINmeuRSdtAxDXU7TlxijRwm0Bo7NUhEPdhG4NSh5LXFthGghewQaylWZLvsHQMczHN0R4i_TABn_jRC3Jui09kZjWqFVNzqRnDW6FHBIBottZJKINoyXZGPy67nGH6-YMrmEF6iL_JN3WjWlefprqDEgrIxpBRxervKmTnZYhZbzMkW82pLYel_WNZlOD0zR3Dzf7jXC9ch4ts1fVLPavEb5r-baw
CODEN	IAECCG
CitedBy_id	crossref_primary_10_3390_life13091828 crossref_primary_10_1142_S0219649222500794 crossref_primary_10_1016_j_ijcard_2024_132506 crossref_primary_10_1007_s10115_025_02361_1 crossref_primary_10_3390_app15031552 crossref_primary_10_3233_IDA_211519 crossref_primary_10_1109_ACCESS_2024_3454516 crossref_primary_10_1007_s11831_025_10406_5 crossref_primary_10_1007_s13755_024_00329_z
Cites_doi	10.1155/2017/1827016 10.1186/1471-2105-14-106 10.1016/j.engappai.2016.02.011 10.1093/oso/9780198714934.003.0003 10.1109/TPAMI.2007.70740 10.1016/j.ehj.2017.01.005 10.1109/ICTAI.2018.00030 10.1007/11538059_91 10.1016/j.eswa.2020.113334 10.1145/2911451.2914722 10.1080/03007995.2019.1646000 10.1016/j.ijbiomac.2016.11.037 10.1007/s00357-005-0018-3 10.1109/ICTAI.2010.49
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2021.3057693
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Open Access Full Text
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	25404
ExternalDocumentID	oai_doaj_org_article_673561c9431049d6ab349596563eec67 10_1109_ACCESS_2021_3057693 9349502
Genre	orig-research
GrantInformation_xml	– fundername: National Key Research and Development Project of China grantid: 2018YFB2101300
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c408t-e3b909d1558a809a27df15e2e1afa99e86b5ee9bc43ef8e512ce743a9358ee253
IEDL.DBID	RIE
ISICitedReferencesCount	21
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000619314000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:52:32 EDT 2025 Sun Jun 29 16:10:59 EDT 2025 Sat Nov 29 06:11:57 EST 2025 Tue Nov 18 22:13:27 EST 2025 Wed Aug 27 05:45:08 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-e3b909d1558a809a27df15e2e1afa99e86b5ee9bc43ef8e512ce743a9358ee253
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8959-1983 0000-0002-2852-0847
OpenAccessLink	https://ieeexplore.ieee.org/document/9349502
PQID	2490800098
PQPubID	4845423
PageCount	11
ParticipantIDs	doaj_primary_oai_doaj_org_article_673561c9431049d6ab349596563eec67 crossref_primary_10_1109_ACCESS_2021_3057693 proquest_journals_2490800098 crossref_citationtrail_10_1109_ACCESS_2021_3057693 ieee_primary_9349502
PublicationCentury	2000
PublicationDate	20210000 2021-00-00 20210101 2021-01-01
PublicationDateYYYYMMDD	2021-01-01
PublicationDate_xml	– year: 2021 text: 20210000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2021
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 nagul (ref15) 2018; 6 de winter (ref28) 2013; 18 ref14 letters (ref26) 1999 ref2 junfan (ref1) 2019 ref17 ref16 ref19 ref18 honglian (ref24) 0 rongfeng (ref3) 0 week (ref6) 2016 yuejin (ref10) 0 jianping (ref11) 0 xiaoli (ref25) 2011 ref22 ref21 yuqiong (ref23) 2008 yaowang (ref12) 0 ref27 ref8 he (ref20) 2012 ref7 fenglan (ref9) 2001 ref4 ref5
References_xml	– ident: ref18 doi: 10.1155/2017/1827016 – year: 2016 ident: ref6 article-title: Heart disorders and diseases; Data on heart attack described by researchers at capital medical University (over expression of protein kinase C epsilon improves retention and survival of transplanted mesenchymal stem cells in rat acute myocardial infarction) – ident: ref17 doi: 10.1186/1471-2105-14-106 – ident: ref14 doi: 10.1016/j.engappai.2016.02.011 – year: 0 ident: ref3 article-title: A report of 9 cases of acute stroke complicated with acute myocardial infarction publication-title: Hunan Medicine – ident: ref13 doi: 10.1093/oso/9780198714934.003.0003 – ident: ref21 doi: 10.1109/TPAMI.2007.70740 – ident: ref4 doi: 10.1016/j.ehj.2017.01.005 – ident: ref8 doi: 10.1109/ICTAI.2018.00030 – ident: ref19 doi: 10.1007/11538059_91 – year: 0 ident: ref10 article-title: Spontaneous improvement of exercise abnormality in acute myocardial infarction and the predictive value of low-dose dobutamine echocardiographic test publication-title: Chinese Journal of circulation – ident: ref7 doi: 10.1016/j.eswa.2020.113334 – year: 2019 ident: ref1 publication-title: Observation of Clinical Effect of 'Stroke Integration' in the Treatment of Ischemic Stroke – year: 0 ident: ref12 article-title: Application of machine learning algorithm in prediction of coronary heart disease and myocardial infarction publication-title: International Medicine & Health Guidance News – ident: ref16 doi: 10.1145/2911451.2914722 – volume: 18 start-page: 10 year: 2013 ident: ref28 article-title: Using the student's t-test with extremely small sample sizes publication-title: Practical Assessment Res Eval – year: 1999 ident: ref26 article-title: An empirical comparison of four initialization methods for the K-means algorithm – ident: ref2 doi: 10.1080/03007995.2019.1646000 – start-page: 1322 year: 2012 ident: ref20 article-title: ADASYN: Adaptive synthetic sampling approach for imbalanced learning publication-title: Proc IEEE World Congr Comput Intell – volume: 6 start-page: 65 year: 2018 ident: ref15 article-title: An effective K-means approach for imbalance data clustering using precise reduction sampling publication-title: Int J Comput Sci Eng – year: 0 ident: ref11 article-title: Analysis of the predictive value of high-frequency electrocardiogram on acute myocardial infarction publication-title: Biomed Eng Res – year: 0 ident: ref24 article-title: Diagnostic value of cardiac troponin T for acute myocardial infarction publication-title: Contemporary Medicine – year: 2011 ident: ref25 publication-title: Research on the Application Value of High-Sensitivity Troponin T Detection in Myocardial Infarction – year: 2008 ident: ref23 article-title: Diagnostic significance of determination of troponin I and T for acute myocardial infarction publication-title: Experimental and Laboratory Medicine – ident: ref5 doi: 10.1016/j.ijbiomac.2016.11.037 – ident: ref27 doi: 10.1007/s00357-005-0018-3 – year: 2001 ident: ref9 publication-title: Electrocardiogram QTcd Changes and Prognosis in Different Periods of Acute Myocardial Infarction – ident: ref22 doi: 10.1109/ICTAI.2010.49
SSID	ssj0000816957
Score	2.3843453
Snippet	Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	25394
SubjectTerms	Algorithms Cardiac arrest Classifiers Clustering Clustering algorithms Data analysis Data processing Feature extraction Heart heart attack Heart attacks imbalanced data Intensive care Machine learning Oversampling Patients Performance prediction Prediction algorithms Predictive models Stroke Stroke (medical condition) Undersampling
SummonAdditionalLinks	– databaseName: DOAJ Open Access Full Text dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA4iHvQgPnF9kYNHq23T5nGsq6IgIqjgRUKanaqoXdmt_n5n2risCHrx2qSPzEwy32TSbxjbIxCdVLmLtHA6yiTOOaPyLJJ5qUBIojhrNX2hLi_13Z25mir1RWfCOnrgTnCHUgl08d6go0MwO5CuFIjpDcIQAeBl-x95rMxUMNWuwTqRJleBZiiJzWHR7-OIMCBMkwO0cSoB-M0VtYz9ocTKj3W5dTanS2wxoERedF-3zGagXmELU9yBq-y-qPn5a0knEz0MomPXOB5O_WM7L14ehhj3P75yRKUcUR42Uk6G9MCHFT9DC2940TTOP_Onml83o-EzdupoVsdr7Pb05KZ_FoVaCZHPYt1EIEoTmwGiA-10bFyqBlWSQwqJq5wxoGWZA5jSZwIqDejmPSB4cJQGBUhzsc5m62ENG4wrn2QgpVapBKo_pwnleCkVlJmIQfVY-iU26wORONWzeLFtQBEb28nakqxtkHWP7U9ueut4NH7vfkT6mHQlEuz2ApqGDaZh_zKNHlslbU4eYqg5Tnts-0u7NkzYsU0pAUqAU2_-x6u32DwNp9ur2Wazzegddtic_2iexqPd1lY_ASIU5Yc priority: 102 providerName: Directory of Open Access Journals
Title	An Imbalanced-Data Processing Algorithm for the Prediction of Heart Attack in Stroke Patients
URI	https://ieeexplore.ieee.org/document/9349502 https://www.proquest.com/docview/2490800098 https://doaj.org/article/673561c9431049d6ab349596563eec67
Volume	9
WOSCitedRecordID	wos000619314000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwELVKxQEOpbAgtpTKB47NNp_-OKbbrooEFRIg7QVFjjOBVduk2nV75Lcz47gRiKpSL1EU25GT50neeOw3jH0gEp20hYlUZlSUC7Q5LYs8EkUtIRMkceaR_iTPz9Vyqb9sscNxLwwA-MVnMKNTH8tventDU2VHOkM6T8qRT6QUw16tcT6FEkjoQgZhoSTWR-V8js-ALmCazHBUU9K_f34-XqM_JFX570vsfy-LF4_r2C7bCTSSlwPuL9kWdK_Y87_EBSfsR9nxj1c1LV200EQnxhketgVgOS8vf_brlft1xZG2cqSBWEhBGwKK9y0_QxNwvHTO2Au-6vhXt-4vsNKgw7p5zb4vTr_Nz6KQTCGyeaxcBFmtY90gfVBGxdqksmmTAlJITGu0BiXqAkDXNs-gVYA8wAKyC0NxUoC0yN6w7a7v4C3j0iY5CKFkKoAS1CmiQVYICXWexSCnLL17y5UNSuOU8OKy8h5HrKsBmoqgqQI0U3Y4NroehDYern5M8I1VSSXbX0BcqmB0tGYN6aHVSJLQEWqEqQknjRQ2A7ACOzohLMebBBinbP9uMFTBojdVShFSYqRq7_5W79gz6uAwPbPPtt36Bt6zp_bWrTbrA-_r4_Hz79MDP3D_ALSD5mE
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwELWqUgk4QKFULJTiA8emzac_jmGh2qrLCoki9YIsx5m0q7YJ2nX5_cwkbgQCVeotiu3IyfPEzzP2G8Y-EIlOmsJGKrMqygXanJZFHomikpAJkjjrkZ7LxUKdn-uvG-xgPAsDAP3mMzikyz6WX3fullxlRzpDOk_KkY-KPE_j4bTW6FGhFBK6kEFaKIn1UTmd4lvgIjBNDnFcU9q_v6afXqU_pFX551_cTzDHzx_WtW32LBBJXg7Iv2Ab0L5kT_-QF9xhP8qWn9xUtHnRQR19st7ycDAAy3l5fdGtlv7yhiNx5UgEsZDCNgQV7xo-QyPwvPTeuiu-bPk3v-qusNKgxLp-xb4ffz6bzqKQTiFyeax8BFmlY10jgVBWxdqmsm6SAlJIbGO1BiWqAkBXLs-gUYBMwAHyC0uRUoC0yHbZZtu18Jpx6ZIchFAyFUAp6hQRISeEhCrPYpATlt59ZeOC1jilvLg2_Zoj1maAxhA0JkAzYQdjo5-D1Mb91T8SfGNV0snubyAuJpgd7VpDgug00iRcCtXCVoSTRhKbATiBHd0hLMeHBBgnbO9uMJhg02uTUoyUOKl68_9W79nj2dmXuZmfLE7fsifU2cFZs8c2_eoW3rEt98sv16v9fuD-Bvmq54I
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Imbalanced-Data+Processing+Algorithm+for+the+Prediction+of+Heart+Attack+in+Stroke+Patients&rft.jtitle=IEEE+access&rft.au=Wang%2C+Meng&rft.au=Yao%2C+Xinghua&rft.au=Chen%2C+Yixiang&rft.date=2021&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=9&rft.spage=25394&rft.epage=25404&rft_id=info:doi/10.1109%2FACCESS.2021.3057693&rft.externalDocID=9349502
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon