{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:51:37Z","timestamp":1782154297097,"version":"3.54.5"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012829","name":"Guangdong Provincial Hospital of Traditional Chinese Medicine","doi-asserted-by":"publisher","award":["YN2024MS021"],"award-info":[{"award-number":["YN2024MS021"]}],"id":[{"id":"10.13039\/100012829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Hainan Province Natural Science Foundation","doi-asserted-by":"publisher","award":["726MS0458"],"award-info":[{"award-number":["726MS0458"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012236","name":"Beijing Institute of Technology Excellent Young Scientists Fund Project","doi-asserted-by":"publisher","award":["XSQD-202216004"],"award-info":[{"award-number":["XSQD-202216004"]}],"id":[{"id":"10.13039\/501100012236","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72271027"],"award-info":[{"award-number":["72271027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.knosys.2026.116480","type":"journal-article","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T22:02:24Z","timestamp":1781474544000},"page":"116480","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Causal-prior-informed prediction of coronary heart disease: Leveraging LLM-guided structure learning and imbalance-aware augmentation"],"prefix":"10.1016","volume":"349","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0250-4707","authenticated-orcid":false,"given":"Liang","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3332-7487","authenticated-orcid":false,"given":"Yujian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116480_bib0001","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.knosys.2016.07.004","article-title":"Coronary artery disease detection using computational intelligence methods","volume":"109","author":"Alizadehsani","year":"2016","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.knosys.2026.116480_bib0002","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.jbi.2018.03.016","article-title":"Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment","volume":"83","author":"Sabahi","year":"2018","journal-title":"J. Biomed. Inf."},{"key":"10.1016\/j.knosys.2026.116480_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102289","article-title":"Machine learning-based heart disease diagnosis: a systematic literature review","volume":"128","author":"Ahsan","year":"2022","journal-title":"Artif. Intell. Med."},{"issue":"3","key":"10.1016\/j.knosys.2026.116480_bib0004","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.2687","article-title":"Association of the interaction between familial hypercholesterolemia variants and adherence to a healthy lifestyle with risk of coronary artery disease","volume":"5","author":"Fahed","year":"2022","journal-title":"JAMA Netw. Open."},{"issue":"16","key":"10.1016\/j.knosys.2026.116480_bib0005","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1001\/jama.2010.461","article-title":"Coronary artery calcium score and risk classification for Coronary heart disease prediction","volume":"303","author":"Polonsky","year":"2010","journal-title":"JAMA-J. Am. Med. Assoc."},{"key":"10.1016\/j.knosys.2026.116480_bib0006","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.inffus.2020.06.008","article-title":"A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion","volume":"63","author":"Ali","year":"2020","journal-title":"Inf. Fusion."},{"key":"10.1016\/j.knosys.2026.116480_bib0007","doi-asserted-by":"crossref","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","article-title":"Effective heart disease prediction using hybrid machine learning techniques","volume":"7","author":"Mohan","year":"2019","journal-title":"IEEE Access."},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0008","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1186\/s12911-019-1004-8","article-title":"Comparing different supervised machine learning algorithms for disease prediction","volume":"19","author":"Uddin","year":"2019","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"10.1016\/j.knosys.2026.116480_bib0009","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.2147\/RMHP.S472398","article-title":"Application of the unbalanced ensemble algorithm for prognostic prediction outcomes of all-cause mortality in coronary heart disease patients comorbid with hypertension","volume":"Volume 17","author":"Zan","year":"2024","journal-title":"Risk Manag. Heal. Policy"},{"key":"10.1016\/j.knosys.2026.116480_bib0010","author":"Bonifacio"},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0011","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-93675-1","article-title":"Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy","volume":"15","author":"Rimal","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0012","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-19587-2","article-title":"Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment","volume":"15","author":"Rehman","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2026.116480_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.107952","article-title":"Predicting coronary heart disease in Chinese diabetics using machine learning","volume":"169","author":"Ma","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.116480_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106641","article-title":"Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals","volume":"155","author":"Wang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.116480_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107637","article-title":"Deep learning based coronary artery disease detection and segmentation using ultrasound imaging with adaptive gated SCNN models","volume":"105","author":"Singh","year":"2025","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.knosys.2026.116480_bib0016","series-title":"Materials Today: Proceedings","article-title":"WITHDRAWN: survey on - identification of coronary artery disease using Deep Learning","author":"JayaSree","year":"2020"},{"key":"10.1016\/j.knosys.2026.116480_bib0017","series-title":"ECG datasets Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing","article-title":"Automated coronary artery disease detection using deep learning on","author":"Yue","year":"2023"},{"issue":"4","key":"10.1016\/j.knosys.2026.116480_bib0018","doi-asserted-by":"crossref","first-page":"186","DOI":"10.33411\/IJIST\/2021030405","article-title":"Activity detection of elderly people using smartphone accelerometer and machine learning methods","volume":"3","author":"Khan","year":"2021","journal-title":"Int. J. Innov. Sci. Technol."},{"issue":"2","key":"10.1016\/j.knosys.2026.116480_bib0019","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3390\/diagnostics14020144","article-title":"Machine learning-based predictive models for detection of cardiovascular diseases","volume":"14","author":"Ogunpola","year":"2024","journal-title":"Diagnostics"},{"issue":"5","key":"10.1016\/j.knosys.2026.116480_bib0020","doi-asserted-by":"crossref","DOI":"10.3390\/math11051081","article-title":"A review on nature-inspired algorithms for cancer disease prediction and classification [Review]","volume":"11","author":"Yaqoob","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.knosys.2026.116480_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113408","article-title":"An efficient convolutional neural network for coronary heart disease prediction","volume":"159","author":"Dutta","year":"2020","journal-title":"Expert. Syst. Appl."},{"issue":"5","key":"10.1016\/j.knosys.2026.116480_bib0022","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/JPROC.2021.3058954","article-title":"Toward causal representation learning","volume":"109","author":"Sch\u00f6lkopf","year":"2021","journal-title":"Proc. IEEE"},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0023","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s40537-021-00492-0","article-title":"Text data augmentation for deep learning","volume":"8","author":"Shorten","year":"2021","journal-title":"J. Big. Data"},{"key":"10.1016\/j.knosys.2026.116480_bib0024","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.ins.2021.02.056","article-title":"A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data","volume":"572","author":"Xu","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.knosys.2026.116480_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2025.114404","article-title":"An interpretable imbalance ensemble classification method for readmission risk assessment incorporating multi-view perturbation and SHAP analysis","volume":"190","author":"Cui","year":"2025","journal-title":"Decis. Support. Syst."},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0026","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s12874-023-01920-w","article-title":"Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms","volume":"23","author":"Hassanzadeh","year":"2023","journal-title":"BMC. Med. Res. Methodol."},{"issue":"4","key":"10.1016\/j.knosys.2026.116480_bib0027","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1080\/07421222.2021.1990619","article-title":"First, do no harm: predictive analytics to reduce in-hospital adverse events","volume":"38","author":"Lin","year":"2021","journal-title":"J. Manage Inf. Syst."},{"key":"10.1016\/j.knosys.2026.116480_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102587","article-title":"Handling missing values in healthcare data: a systematic review of deep learning-based imputation techniques","volume":"142","author":"Liu","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.knosys.2026.116480_bib0029","article-title":"Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV","volume":"144","author":"Nicholas","year":"2023","journal-title":"J. Biomed. Inf."},{"key":"10.1016\/j.knosys.2026.116480_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112745","article-title":"Data augmentation based on large language models for radiological report classification","volume":"308","author":"Collado-Monta\u00f1ez","year":"2025","journal-title":"Knowl. Based. Syst."},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0031","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41746-023-00958-w","article-title":"A study of generative large language model for medical research and healthcare","volume":"6","author":"Peng","year":"2023","journal-title":"NPJ. Digit. Med."},{"key":"10.1016\/j.knosys.2026.116480_bib0032","series-title":"AMIA Annual Symposium Proceedings","article-title":"Large language models for healthcare data augmentation: an example on patient-trial matching","author":"Yuan","year":"2024"},{"issue":"2","key":"10.1016\/j.knosys.2026.116480_bib0033","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3703155","article-title":"A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions","volume":"43","author":"Huang","year":"2025","journal-title":"ACM. Trans. Inf. Syst."},{"key":"10.1016\/j.knosys.2026.116480_bib0034","doi-asserted-by":"crossref","first-page":"31504","DOI":"10.52202\/079017-0990","article-title":"Epic: effective prompting for imbalanced-class data synthesis in tabular data classification via large language models","volume":"37","author":"Kim","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116480_bib0035","series-title":"Curated LLM: synergy of LLMs and data curation for tabular augmentation in low-data regimes Proceedings of the 41st International Conference on Machine Learning","author":"Seedat","year":"2024"},{"issue":"2","key":"10.1016\/j.knosys.2026.116480_bib0036","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s42256-022-00445-z","article-title":"Stable learning establishes some common ground between causal inference and machine learning","volume":"4","author":"Cui","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116480_bib0037","article-title":"Probing digital footprints and reaching for inherent preferences: a cause-disentanglement approach to personalized recommendations","author":"Wang","year":"2024","journal-title":"Inf. Syst. Res."},{"key":"10.1016\/j.knosys.2026.116480_bib0038","author":"Kaddour"},{"key":"10.1016\/j.knosys.2026.116480_bib0039","series-title":"Causality","author":"Pearl","year":"2009"},{"key":"10.1016\/j.knosys.2026.116480_bib0040","author":"Cohrs"},{"key":"10.1016\/j.knosys.2026.116480_bib0041","doi-asserted-by":"crossref","first-page":"ii87","DOI":"10.1093\/bioinformatics\/btae411","article-title":"A hybrid constrained continuous optimization approach for optimal causal discovery from biological data","volume":"40","author":"Zhu","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.knosys.2026.116480_bib0042","doi-asserted-by":"crossref","DOI":"10.52202\/079017-4231","article-title":"Hybrid top-down global causal discovery with local search for linear and nonlinear additive noise models","author":"Hiremath","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116480_bib0043","series-title":"Proceedings of the Second Conference on Causal Learning and Reasoning, Proceedings of Machine Learning Research","article-title":"Scalable causal discovery with score matching","author":"Montagna","year":"2023"},{"key":"10.1016\/j.knosys.2026.116480_bib0044","series-title":"Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence","article-title":"Reinforcement causal structure learning on order graph","author":"Yang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116480_bib0045","author":"Wang"},{"key":"10.1016\/j.knosys.2026.116480_bib0046","author":"Larsen"},{"key":"10.1016\/j.knosys.2026.116480_bib0047","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Effective causal discovery under identifiable heteroscedastic noise model","author":"Yin","year":"2024"},{"key":"10.1016\/j.knosys.2026.116480_bib0048","doi-asserted-by":"crossref","first-page":"47432","DOI":"10.52202\/079017-1504","article-title":"CausalStock: deep end-to-end causal discovery for news-driven multi-stock movement prediction","volume":"37","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116480_bib0049","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ins.2022.02.038","article-title":"SMOTE-RkNN: a hybrid re-sampling method based on SMOTE and reverse k-nearest neighbors","volume":"595","author":"Zhang","year":"2022","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.knosys.2026.116480_bib0050","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.knosys.2026.116480_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.112754","article-title":"Enhancing dementia prediction models: leveraging temporal patterns and class methods","volume":"171","author":"Seixas","year":"2025","journal-title":"Appl. Soft. Comput."},{"key":"10.1016\/j.knosys.2026.116480_bib0052","series-title":"Proceedings of Workshop on Learning from Imbalanced Datasets","article-title":"KNN approach to unbalanced data distributions: a case study involving information extraction","author":"Mani","year":"2003"},{"key":"10.1016\/j.knosys.2026.116480_bib0053","author":"Arpit"},{"key":"10.1016\/j.knosys.2026.116480_bib0054","series-title":"Proceedings of Machine Learning Research","article-title":"Local constraint-based causal discovery under selection bias proceedings of the First Conference on Causal learning and reasoning","author":"Versteeg","year":"2022"},{"key":"10.1016\/j.knosys.2026.116480_bib0055","article-title":"Direct medical costs of ischemic heart disease in urban Southern China: a 5-year retrospective analysis of an all-payer health claims database in Guangzhou City","volume":"11","author":"Xie","year":"2023","journal-title":"Front. Public Heal."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126012062?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126012062?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:24:30Z","timestamp":1782152670000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126012062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":55,"alternative-id":["S0950705126012062"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116480","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Causal-prior-informed prediction of coronary heart disease: Leveraging LLM-guided structure learning and imbalance-aware augmentation","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116480","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116480"}}