A stacking ensemble model for predicting the occurrence of carotid atherosclerosis
Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related mark...
Uloženo v:
| Vydáno v: | Frontiers in endocrinology (Lausanne) Ročník 15; s. 1390352 |
|---|---|
| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
Frontiers Media S.A
23.07.2024
|
| Témata: | |
| ISSN: | 1664-2392, 1664-2392 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers.
Based on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables.
A total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors.
The ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases. |
|---|---|
| AbstractList | BackgroundCarotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers.MethodsBased on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables.ResultsA total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors.ConclusionThe ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases. Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers. Based on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables. A total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors. The ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases. Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers.BackgroundCarotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers.Based on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables.MethodsBased on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables.A total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors.ResultsA total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors.The ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases.ConclusionThe ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases. |
| Author | Yang, Wangxuan Tang, Chuanping Liu, Wei Wang, Qinghuan Wang, Shuohuan Wang, Di Zhang, Xiaoshuai Tang, Fang |
| AuthorAffiliation | 1 Department of Data Science, School of Statistics and Mathematics, Shandong University of Finance and Economics , Jinan , China 3 Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering Research Center of Diagnosis and Treatment Technology for Bariatric and Metabolism-Associated Surgery , Jinan , China 6 Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University , Jinan , China 5 Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital Shandong Data Open Innovative Application Laboratory , Jinan , China 4 School of Public Health, Harbin Medical University , Harbin , China 2 Information Technology Division, Shandong International Trust Co., Ltd. , Jinan , China |
| AuthorAffiliation_xml | – name: 4 School of Public Health, Harbin Medical University , Harbin , China – name: 1 Department of Data Science, School of Statistics and Mathematics, Shandong University of Finance and Economics , Jinan , China – name: 6 Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University , Jinan , China – name: 2 Information Technology Division, Shandong International Trust Co., Ltd. , Jinan , China – name: 5 Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital Shandong Data Open Innovative Application Laboratory , Jinan , China – name: 3 Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering Research Center of Diagnosis and Treatment Technology for Bariatric and Metabolism-Associated Surgery , Jinan , China |
| Author_xml | – sequence: 1 givenname: Xiaoshuai surname: Zhang fullname: Zhang, Xiaoshuai – sequence: 2 givenname: Chuanping surname: Tang fullname: Tang, Chuanping – sequence: 3 givenname: Shuohuan surname: Wang fullname: Wang, Shuohuan – sequence: 4 givenname: Wei surname: Liu fullname: Liu, Wei – sequence: 5 givenname: Wangxuan surname: Yang fullname: Yang, Wangxuan – sequence: 6 givenname: Di surname: Wang fullname: Wang, Di – sequence: 7 givenname: Qinghuan surname: Wang fullname: Wang, Qinghuan – sequence: 8 givenname: Fang surname: Tang fullname: Tang, Fang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39109079$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9UUtr3DAQFiGlSdP8gRyKj73sVi_b0qmE0EcgUCjJWYyl8UapLW0lbaH_vnJ2G5IeqoNm0Mz3QN8bchxiQEIuGF0LofSHEYOLa065XDOhqWj5ETllXSdXXGh-_Kw_Iec5P9B6JGVaq9fkRGhGNe31Kfl-2eQC9ocPmwZDxnmYsJmjw6kZY2q2CZ23ZZmWe2yitbuUMNjajo2FFIt3DdRRitlOy-3zW_JqhCnj-aGekbvPn26vvq5uvn25vrq8WVnZ6bIaWTfq3ukeJRUMBzfKjmkureuEBWCi7zRznRU4gqMOnVXOKVASkXLNenFGrve8LsKD2SY_Q_ptInjz-BDTxkAqvtoyrIeWthYUdVoOjutBKttzZVmtA0Dl-rjn2u6GuUphKAmmF6QvJ8Hfm038ZRgTtGbQVob3B4YUf-4wFzP7bHGaIGDcZSOo0kox2i7G3z0Xe1L5m0pd4PsFWz80JxyfVhg1S_rmMX2zpG8O6VeQ-gdkfYHi42LYT_-D_gGIird7 |
| CitedBy_id | crossref_primary_10_1186_s42492_025_00204_y |
| Cites_doi | 10.1186/s12859-023-05300-5 10.1097/MD.0000000000003864 10.1016/j.ijmedinf.2020.104326 10.1136/bmjopen-2013-004498 10.1016/j.compbiolchem.2023.107973 10.1016/j.compbiomed.2016.06.019 10.1093/aje/kwad113 10.1038/ajg.2013.332 10.3389/fneur.2018.00784 10.1136/svn-2017-000101 10.1186/s12874-024-02179-5 10.1186/s12911-022-01767-z 10.1002/clc.22934 10.1007/s12010-019-03222-8 10.1186/s12859-020-03847-1 10.1093/bib/bbaa049 10.1161/STROKEAHA.112.673129 10.3389/fgene.2021.600040 10.1002/cam4.4617 10.1212/WNL.44.6.1046 10.1002/mp.13361 10.1111/acps.13061 10.1007/s10115-013-0679-x 10.1038/s41591-020-0951-z 10.1007/s11657-020-00802-8 10.1186/s12859-024-05714-9 10.1161/01.CIR.0000018650.58984.75 10.3748/wjg.v28.i46.6551 10.1007/s10654-018-0390-z 10.1080/10255842.2022.2072683 10.1016/j.redox.2018.09.025 10.1186/s12911-021-01480-3 10.1016/j.jvs.2019.04.488 10.2202/1544-6115.1309 10.1111/1759-7714.13204 |
| ContentType | Journal Article |
| Copyright | Copyright © 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang. Copyright © 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang |
| Copyright_xml | – notice: Copyright © 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang. – notice: Copyright © 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM DOA |
| DOI | 10.3389/fendo.2024.1390352 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1664-2392 |
| ExternalDocumentID | oai_doaj_org_article_17a505ca80d94bd29b48c728c148cbaa PMC11300245 39109079 10_3389_fendo_2024_1390352 |
| Genre | Journal Article |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ACGFO ACGFS ADBBV ADRAZ AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK EMOBN GROUPED_DOAJ GX1 HYE KQ8 M48 M~E OK1 PGMZT RPM ACXDI CGR CUY CVF ECM EIF IPNFZ NPM RIG 7X8 5PM |
| ID | FETCH-LOGICAL-c469t-f16f97d97e4031ebdf461924cd63caa137691d6c3efad0dedc8dd8a84ee029173 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001283740100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1664-2392 |
| IngestDate | Fri Oct 03 12:46:08 EDT 2025 Thu Aug 21 18:31:58 EDT 2025 Fri Sep 05 11:59:27 EDT 2025 Thu Apr 03 07:01:43 EDT 2025 Sat Nov 29 06:41:21 EST 2025 Tue Nov 18 21:00:55 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | carotid atherosclerosis prediction endocrine-related markers machine learning stacking |
| Language | English |
| License | Copyright © 2024 Zhang, Tang, Wang, Liu, Yang, Wang, Wang and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-f16f97d97e4031ebdf461924cd63caa137691d6c3efad0dedc8dd8a84ee029173 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Eliza Russu, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Târgu Mureş, Romania Reviewed by: Niranjana Sampathila, Manipal Academy of Higher Education, India Stoian Adina, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Târgu Mureş, Romania |
| OpenAccessLink | https://doaj.org/article/17a505ca80d94bd29b48c728c148cbaa |
| PMID | 39109079 |
| PQID | 3089881057 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_17a505ca80d94bd29b48c728c148cbaa pubmedcentral_primary_oai_pubmedcentral_nih_gov_11300245 proquest_miscellaneous_3089881057 pubmed_primary_39109079 crossref_primary_10_3389_fendo_2024_1390352 crossref_citationtrail_10_3389_fendo_2024_1390352 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-07-23 |
| PublicationDateYYYYMMDD | 2024-07-23 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-07-23 day: 23 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in endocrinology (Lausanne) |
| PublicationTitleAlternate | Front Endocrinol (Lausanne) |
| PublicationYear | 2024 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Ahmed (B31) 2024; 25 Sirimarco (B1) 2013; 44 Jiang (B15) 2017; 2 Singal (B25) 2013; 108 Fine-Edelstein (B35) 1994; 44 Danielsen (B10) 2019; 140 Taylor (B4) 2014; 4 Verma (B18) 2020; 191 van der Laan (B27) 2007; 6 van den Munckhof (B5) 2018; 41 Kop (B24) 2016; 76 Byra (B9) 2019; 46 Liu (B23) 2022; 11 Dalal (B29) 2022; 28 Kapila (B33) 2023; 107 Liang (B16) 2022; 22 Jiang (B8) 2021; 145 Shim (B13) 2020; 15 Fan (B36) 2021; 21 van Os (B14) 2018; 9 Liang (B17) 2021; 12 Zhou (B34) 2023; 24 Zhu (B20) 2024; 24 Wu (B6) 2016; 95 Martinez (B2) 2020; 71 Yu (B11) 2020; 11 Schultebraucks (B12) 2020; 26 Hollander (B3) 2002; 105 Biswas (B32) 2023; 26 Gantenberg (B19) 2023; 192 Li (B21) 2021; 22 Naimi (B28) 2018; 33 Štrumbelj (B30) 2014; 41 Yuan (B7) 2019; 20 Xu (B26) 2020; 21 Lundberg (B22) 2017 |
| References_xml | – volume: 24 start-page: 224 year: 2023 ident: B34 article-title: A diabetes prediction model based on Boruta feature selection and ensemble learning publication-title: BMC Bioinf doi: 10.1186/s12859-023-05300-5 – volume: 95 year: 2016 ident: B6 article-title: Influence of blood pressure variability on early carotid atherosclerosis in hypertension with and without diabetes publication-title: Med (Baltimore) doi: 10.1097/MD.0000000000003864 – volume: 145 year: 2021 ident: B8 article-title: Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease publication-title: Int J Med Inform doi: 10.1016/j.ijmedinf.2020.104326 – volume: 4 year: 2014 ident: B4 article-title: Influence of chronic exercise on carotid atherosclerosis in marathon runners publication-title: BMJ Open doi: 10.1136/bmjopen-2013-004498 – volume: 107 year: 2023 ident: B33 article-title: Optimizing fetal health prediction: Ensemble modeling with fusion of feature selection and extraction techniques for cardiotocography data publication-title: Comput Biol Chem doi: 10.1016/j.compbiolchem.2023.107973 – volume: 76 year: 2016 ident: B24 article-title: Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2016.06.019 – volume: 192 year: 2023 ident: B19 article-title: Predicting seasonal influenza hospitalizations using an ensemble super learner: A simulation study publication-title: Am J Epidemiol doi: 10.1093/aje/kwad113 – volume: 108 year: 2013 ident: B25 article-title: Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma publication-title: Am J Gastroenterol doi: 10.1038/ajg.2013.332 – volume: 9 year: 2018 ident: B14 article-title: Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms publication-title: Front Neurol doi: 10.3389/fneur.2018.00784 – volume: 2 year: 2017 ident: B15 article-title: Artificial intelligence in healthcare: past, present and future publication-title: Stroke Vasc Neurol doi: 10.1136/svn-2017-000101 – volume: 24 start-page: 59 year: 2024 ident: B20 article-title: Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction publication-title: BMC Med Res Methodol doi: 10.1186/s12874-024-02179-5 – volume: 22 start-page: 27 year: 2022 ident: B16 article-title: Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder publication-title: BMC Med Inform Decis Mak doi: 10.1186/s12911-022-01767-z – volume: 41 start-page: 698 year: 2018 ident: B5 article-title: Relation between age and carotid artery intima-medial thickness: a systematic review publication-title: Clin Cardiol doi: 10.1002/clc.22934 – volume: 191 year: 2020 ident: B18 article-title: Prediction of skin disease with three different feature selection techniques using stacking ensemble method publication-title: Appl Biochem Biotechnol doi: 10.1007/s12010-019-03222-8 – volume: 21 start-page: 504 year: 2020 ident: B26 article-title: Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost publication-title: BMC Bioinf doi: 10.1186/s12859-020-03847-1 – volume: 22 year: 2021 ident: B21 article-title: Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework publication-title: Brief Bioinform doi: 10.1093/bib/bbaa049 – volume: 44 year: 2013 ident: B1 article-title: Carotid atherosclerosis and risk of subsequent coronary event in outpatients with atherothrombosis publication-title: Stroke doi: 10.1161/STROKEAHA.112.673129 – volume: 12 year: 2021 ident: B17 article-title: A stacking ensemble learning framework for genomic prediction publication-title: Front Genet doi: 10.3389/fgene.2021.600040 – volume: 11 year: 2022 ident: B23 article-title: Prediction of lung metastases in thyroid cancer using machine learning based on SEER database publication-title: Cancer Med doi: 10.1002/cam4.4617 – volume: 44 year: 1994 ident: B35 article-title: Precursors of extracranial carotid atherosclerosis in the Framingham Study publication-title: Neurology doi: 10.1212/WNL.44.6.1046 – volume: 46 year: 2019 ident: B9 article-title: Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion publication-title: Med Phys doi: 10.1002/mp.13361 – volume: 140 year: 2019 ident: B10 article-title: Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data publication-title: Acta Psychiatr Scand doi: 10.1111/acps.13061 – volume: 41 year: 2014 ident: B30 article-title: Explaining prediction models and individual predictions with feature contributions publication-title: Knowl Inf Syst doi: 10.1007/s10115-013-0679-x – volume: 26 year: 2020 ident: B12 article-title: A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor publication-title: Nat Med doi: 10.1038/s41591-020-0951-z – volume: 15 start-page: 169 year: 2020 ident: B13 article-title: Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women publication-title: Arch Osteoporos doi: 10.1007/s11657-020-00802-8 – volume: 25 start-page: 111 year: 2024 ident: B31 article-title: StackDPP: a stacking ensemble based DNA-binding protein prediction model publication-title: BMC Bioinf doi: 10.1186/s12859-024-05714-9 – volume: 105 year: 2002 ident: B3 article-title: Carotid plaques increase the risk of stroke and subtypes of cerebral infarction in asymptomatic elderly: the Rotterdam study publication-title: Circulation doi: 10.1161/01.CIR.0000018650.58984.75 – volume: 28 year: 2022 ident: B29 article-title: Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy publication-title: World J Gastroenterol doi: 10.3748/wjg.v28.i46.6551 – volume: 33 year: 2018 ident: B28 article-title: Stacked generalization: an introduction to super learning publication-title: Eur J Epidemiol doi: 10.1007/s10654-018-0390-z – volume: 26 year: 2023 ident: B32 article-title: Early detection of Parkinson disease using stacking ensemble method publication-title: Comput Methods Biomech BioMed Engin doi: 10.1080/10255842.2022.2072683 – volume: 20 year: 2019 ident: B7 article-title: New insights into oxidative stress and inflammation during diabetes mellitus-accelerated atherosclerosis publication-title: Redox Biol doi: 10.1016/j.redox.2018.09.025 – volume: 21 start-page: 115 year: 2021 ident: B36 article-title: The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models publication-title: BMC Med Inform Decis Mak doi: 10.1186/s12911-021-01480-3 – volume: 71 year: 2020 ident: B2 article-title: Review of serum biomarkers in carotid atherosclerosis publication-title: J Vasc Surg doi: 10.1016/j.jvs.2019.04.488 – year: 2017 ident: B22 article-title: A unified approach to interpreting model predictions – volume: 6 start-page: Article25 year: 2007 ident: B27 article-title: Super learner publication-title: Stat Appl Genet Mol Biol doi: 10.2202/1544-6115.1309 – volume: 11 start-page: 95 year: 2020 ident: B11 article-title: Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier publication-title: Thorac Cancer doi: 10.1111/1759-7714.13204 |
| SSID | ssj0000401998 |
| Score | 2.3543375 |
| Snippet | Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble... BackgroundCarotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 1390352 |
| SubjectTerms | Aged Algorithms Carotid Artery Diseases - epidemiology carotid atherosclerosis Cohort Studies endocrine-related markers Endocrinology Female Humans Machine Learning Male Middle Aged prediction Prognosis Risk Assessment - methods Risk Factors stacking Support Vector Machine |
| Title | A stacking ensemble model for predicting the occurrence of carotid atherosclerosis |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39109079 https://www.proquest.com/docview/3089881057 https://pubmed.ncbi.nlm.nih.gov/PMC11300245 https://doaj.org/article/17a505ca80d94bd29b48c728c148cbaa |
| Volume | 15 |
| WOSCitedRecordID | wos001283740100001&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: 1664-2392 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000401998 issn: 1664-2392 databaseCode: DOA dateStart: 20100101 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: 1664-2392 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000401998 issn: 1664-2392 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEB7SEEovoekrzgsFeituZFtrScc0JPTQhFDasjehJ11IvGF302N-e2dkZ9ktJb30Iowt2_JIsr5P0nwD8J5rF0j1hNzPbClUDKWVbSoR6fORrRW3WZ3_xxd5daXGY329EuqL9oT18sC94U7wEThIe6t40MKFWjuhvKyVRxzvnc3QiEu9QqbyPxhpAxKJ3ksGWZg-SbEL5OxXi48Ienj2NFoZibJg_99Q5p-bJVdGn4uXsD3ARnbaF3cHNmL3Cp5fDgvjr-HrKUOY52nemyExjbfuJrIc5YYhKmV3M8pJO5wZAj429T7LMnk8TIwi9ywmgWUsOJ3j8zGdzN_A94vzb2efyyFcQumR4y7KVLVJy6BlFNhTowtJZHrlQ9t4ayv8legqtL6JyQYe8ItUCMoqESOvkbU1b2Gzm3ZxF1htBfdc-zQSSbg0crR46ZyMXrbkvFpA9Wg64wctcQppcWOQU5C5TTa3IXObwdwFfFjec9craTyZ-xPVyDInqWDnE9g2zNA2zL_aRgHHj_VpsNfQUojt4vR-bhqutFIU47iAd339Ll_VaNqsKnUBaq3m18qyfqWb_MzK3BUtDtZitPc_Sr8PL8giNJFcNwewuZjdx0PY8r8Wk_nsCJ7JsTrKrR7Ty4fz30tCCPU |
| linkProvider | Directory of Open Access Journals |
| 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=A+stacking+ensemble+model+for+predicting+the+occurrence+of+carotid+atherosclerosis&rft.jtitle=Frontiers+in+endocrinology+%28Lausanne%29&rft.au=Zhang%2C+Xiaoshuai&rft.au=Tang%2C+Chuanping&rft.au=Wang%2C+Shuohuan&rft.au=Liu%2C+Wei&rft.date=2024-07-23&rft.issn=1664-2392&rft.eissn=1664-2392&rft.volume=15&rft.spage=1390352&rft_id=info:doi/10.3389%2Ffendo.2024.1390352&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-2392&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-2392&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-2392&client=summon |