Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure
The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratific...
Gespeichert in:
| Veröffentlicht in: | Journal of proteome research Jg. 22; H. 3; S. 758 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
03.03.2023
|
| Schlagworte: | |
| ISSN: | 1535-3907, 1535-3907 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision,
1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases. |
|---|---|
| AbstractList | The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision,
1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases. The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases. |
| Author | Zhang, Lirong Feng, Jianghua Liu, Wuping Shen, Guiping Bao, Lijun |
| Author_xml | – sequence: 1 givenname: Wuping orcidid: 0000-0001-7723-4161 surname: Liu fullname: Liu, Wuping organization: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China – sequence: 2 givenname: Lirong surname: Zhang fullname: Zhang, Lirong organization: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China – sequence: 3 givenname: Lijun surname: Bao fullname: Bao, Lijun organization: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China – sequence: 4 givenname: Guiping orcidid: 0000-0002-0779-1859 surname: Shen fullname: Shen, Guiping organization: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China – sequence: 5 givenname: Jianghua orcidid: 0000-0001-8899-2750 surname: Feng fullname: Feng, Jianghua organization: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36710647$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkElPwzAQhS1URBf4CaAcuaR4iWv3GJWtUisQgnM08UJTJXGx40P_PRYFidPMaL43evOmaNS73iB0TfCcYEruQIX5_uDdYFxn5lRhXEh5hiaEM56zJRajf_0YTUPYY0y4wOwCjdlCELwoxARVpVLRw2CyVQshNLZRMDSuz6DX2as3ulE_o7NZqWLCtkenwOsG2mzdW_Cn9bDzLn7ukior37b3SemU0dGbS3RuoQ3m6rfO0Mfjw_vqOd-8PK1X5SYHJosh1zWrpcYckkNeK1GDUlZoJgwhxRKw1kRiZhkWkppFAVpYIWxBOIilhZrSGbo93U2RfEUThqprgjJtC71xMVRUpI8lFxQn9OYXjXVndHXwTQf-WP2FQr8Bzi5pnw |
| CitedBy_id | crossref_primary_10_1016_j_bspc_2025_107590 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1021/acs.jproteome.2c00488 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Chemistry |
| EISSN | 1535-3907 |
| ExternalDocumentID | 36710647 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- 4.4 53G 55A 5GY 5VS 7~N AABXI AAHBH ABJNI ABMVS ABQRX ABUCX ACGFS ACS ADHLV AEESW AENEX AFEFF AHGAQ ALMA_UNASSIGNED_HOLDINGS AQSVZ BAANH CGR CS3 CUPRZ CUY CVF DU5 EBS ECM ED~ EIF F5P GGK GNL IH9 IHE JG~ NPM P2P RNS ROL UI2 VF5 VG9 W1F 7X8 ABBLG ABLBI |
| ID | FETCH-LOGICAL-a384t-db3b8d05a1575bc7baccf7d37e1149a0dd1803f30782e64ad7f77f415a79fab22 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000925391900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1535-3907 |
| IngestDate | Fri Jul 11 09:14:05 EDT 2025 Thu Jan 02 22:52:33 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | clinical classification machine learning metabolomics nuclear magnetic resonance acute myocardial infarction |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a384t-db3b8d05a1575bc7baccf7d37e1149a0dd1803f30782e64ad7f77f415a79fab22 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-0779-1859 0000-0001-8899-2750 0000-0001-7723-4161 |
| PMID | 36710647 |
| PQID | 2771085720 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2771085720 pubmed_primary_36710647 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-03-03 |
| PublicationDateYYYYMMDD | 2023-03-03 |
| PublicationDate_xml | – month: 03 year: 2023 text: 2023-03-03 day: 03 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Journal of proteome research |
| PublicationTitleAlternate | J Proteome Res |
| PublicationYear | 2023 |
| SSID | ssj0015703 |
| Score | 2.4101791 |
| Snippet | The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 758 |
| SubjectTerms | Humans Machine Learning Magnetic Resonance Imaging Myocardial Infarction - diagnosis Prognosis ROC Curve |
| Title | Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36710647 https://www.proquest.com/docview/2771085720 |
| Volume | 22 |
| WOSCitedRecordID | wos000925391900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwEA_qBH3x-2N-EcHXbl3SNO2TFHUouDFEYW_lmg9QtJ3rKvjfm6QtexIEXwolCbSXS-4ud_n9ELpiFEIJAB4QCL1Am4A1Cm2VuTF2PlGCcOHQ9R_5eBxNp_GkOXArm7LKdk90G7UshD0j7xPObaE8J_717NOzrFE2u9pQaKyiDjWujNVqPl1mESy6VI2XyjwT2_P2Bg8Z9EGUvTcHhVB8qB4RTpN_9zKdtRlu__c7d9BW42fipFaMXbSi8j20cdPSu-2jNBGisjgR2PFi2oohN0kYcoknc5u_ca-FxomoTLfRtzF7Vp3e8UOuzfpwzQ3PjxmFk6fRLXYXD2Q1VwfoZXj3fHPvNWwLHtAoWHgyo1kkfQZGbCwTPAMhNJeUKxMyxeBLOYh8qqn1KVQYgOSac23sP_BYQ0bIIVrLi1wdI-yHisqYxYoE0joMwITIMg7KhL4DplgXXbayS81f2xQF5KqoynQpvS46qicgndWwGykNTVsY8JM_jD5Fm5YX3hWL0TPU0WYtq3O0Lr4Wr-X8wqmJeY4nox_MQsj_ |
| linkProvider | ProQuest |
| 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=Accurate+Classification+and+Prediction+of+Acute+Myocardial+Infarction+through+an+ARMD+Procedure&rft.jtitle=Journal+of+proteome+research&rft.au=Liu%2C+Wuping&rft.au=Zhang%2C+Lirong&rft.au=Bao%2C+Lijun&rft.au=Shen%2C+Guiping&rft.date=2023-03-03&rft.issn=1535-3907&rft.eissn=1535-3907&rft.volume=22&rft.issue=3&rft.spage=758&rft_id=info:doi/10.1021%2Facs.jproteome.2c00488&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1535-3907&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1535-3907&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1535-3907&client=summon |