{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:23:30Z","timestamp":1783074210424,"version":"3.54.6"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100014472","name":"Scientific Research Foundation of Hunan Provincial Education Department","doi-asserted-by":"publisher","award":["25C1710"],"award-info":[{"award-number":["25C1710"]}],"id":[{"id":"10.13039\/100014472","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515010197"],"award-info":[{"award-number":["2025A1515010197"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2026GXNSFAA00641306"],"award-info":[{"award-number":["2026GXNSFAA00641306"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["625RC716"],"award-info":[{"award-number":["625RC716"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018978","name":"Zhejiang Office of Philosophy and Social Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018978","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62462021"],"award-info":[{"award-number":["62462021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110731","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T09:15:15Z","timestamp":1780650915000},"page":"110731","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["IGStrokeNet: An effective and explainable framework for stroke risk prediction"],"prefix":"10.1016","volume":"125","author":[{"given":"Lingling","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9680-9514","authenticated-orcid":false,"given":"Guanru","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-7241","authenticated-orcid":false,"given":"Zhizhe","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-8891","authenticated-orcid":false,"given":"Teng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1177\/17474930211065917","article-title":"World Stroke Organization (WSO): global stroke fact sheet 2022","volume":"17","author":"Feigin","year":"2022","journal-title":"Int. J. Stroke"},{"key":"10.1016\/j.bspc.2026.110731_b2","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2023.1255524","article-title":"Changing trends of disease burden of stroke from 1990 to 2019 and its predictions among the Chinese population","volume":"14","author":"Liang","year":"2023","journal-title":"Front. Neurol."},{"key":"10.1016\/j.bspc.2026.110731_b3","series-title":"Caplan\u2019s Stroke","author":"Caplan","year":"2016"},{"key":"10.1016\/j.bspc.2026.110731_b4","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2021.827522","article-title":"Predicting ischemic stroke outcome using deep learning approaches","volume":"12","author":"Fang","year":"2022","journal-title":"Front. Genet."},{"issue":"12","key":"10.1016\/j.bspc.2026.110731_b5","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1016\/j.amjmed.2021.07.027","article-title":"Ischemic stroke","volume":"134","author":"Feske","year":"2021","journal-title":"Am. J. Med."},{"issue":"10","key":"10.1016\/j.bspc.2026.110731_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105162","article-title":"Machine learning for brain stroke: a review","volume":"29","author":"Sirsat","year":"2020","journal-title":"J. Stroke Cerebrovasc. Dis."},{"issue":"12","key":"10.1016\/j.bspc.2026.110731_b7","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1503\/cmaj.140355","article-title":"Diagnosis and management of acute ischemic stroke: speed is critical","volume":"187","author":"Musuka","year":"2015","journal-title":"Cmaj"},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b8","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.nicl.2012.10.003","article-title":"Medical image analysis methods in MR\/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal","volume":"1","author":"Rekik","year":"2012","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.bspc.2026.110731_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102178","article-title":"Towards effective classification of brain hemorrhagic and ischemic stroke using CNN","volume":"63","author":"Gautam","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"issue":"12","key":"10.1016\/j.bspc.2026.110731_b10","doi-asserted-by":"crossref","first-page":"783","DOI":"10.3390\/bioengineering9120783","article-title":"A deep learning approach for detecting stroke from brain CT images using OzNet","volume":"9","author":"Ozaltin","year":"2022","journal-title":"Bioengineering"},{"key":"10.1016\/j.bspc.2026.110731_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109568","article-title":"Mutual gain adaptive network for segmenting brain stroke lesions","volume":"129","author":"Huang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.bspc.2026.110731_b12","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.mri.2022.09.006","article-title":"Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: a preliminary study based on deep learning","volume":"94","author":"Qiu","year":"2022","journal-title":"Magn. Reson. Imaging"},{"issue":"8","key":"10.1016\/j.bspc.2026.110731_b13","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1093\/jamia\/ocab068","article-title":"Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults","volume":"28","author":"Chun","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"7","key":"10.1016\/j.bspc.2026.110731_b14","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1161\/STROKEAHA.121.036557","article-title":"Prediction of recurrent ischemic stroke using registry data and machine learning methods: the Erlangen stroke registry","volume":"53","author":"Vodencarevic","year":"2022","journal-title":"Stroke"},{"key":"10.1016\/j.bspc.2026.110731_b15","doi-asserted-by":"crossref","first-page":"52288","DOI":"10.1109\/ACCESS.2023.3278273","article-title":"Automated stroke prediction using machine learning: an explainable and exploratory study with a web application for early intervention","volume":"11","author":"Mridha","year":"2023","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.bspc.2026.110731_b16","doi-asserted-by":"crossref","DOI":"10.1002\/hsr2.70062","article-title":"The most efficient machine learning algorithms in stroke prediction: A systematic review","volume":"7","author":"Asadi","year":"2024","journal-title":"Health Sci. Rep."},{"issue":"8","key":"10.1016\/j.bspc.2026.110731_b17","doi-asserted-by":"crossref","first-page":"5188","DOI":"10.3390\/app13085188","article-title":"Decision support system for predicting mortality in cardiac patients based on machine learning","volume":"13","author":"Javeed","year":"2023","journal-title":"Appl. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b18","doi-asserted-by":"crossref","DOI":"10.1111\/1753-0407.70049","article-title":"Overcoming missing data: Accurately predicting cardiovascular risk in type 2 diabetes, a systematic review","volume":"17","author":"Ren","year":"2025","journal-title":"J. Diabetes"},{"issue":"3","key":"10.1016\/j.bspc.2026.110731_b19","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s10479-021-04006-2","article-title":"Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991\u20132020)","volume":"339","author":"Alizadehsani","year":"2024","journal-title":"Ann. Oper. Res."},{"issue":"2","key":"10.1016\/j.bspc.2026.110731_b20","doi-asserted-by":"crossref","first-page":"bbab569","DOI":"10.1093\/bib\/bbab569","article-title":"Multimodal deep learning for biomedical data fusion: a review","volume":"23","author":"Stahlschmidt","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"7","key":"10.1016\/j.bspc.2026.110731_b21","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1161\/01.STR.0000019882.06696.D6","article-title":"MRI: The new gold standard for detecting brain hemorrhage?","volume":"33","author":"von Kummer","year":"2002","journal-title":"Stroke"},{"issue":"24","key":"10.1016\/j.bspc.2026.110731_b22","doi-asserted-by":"crossref","first-page":"9859","DOI":"10.3390\/s22249859","article-title":"Explainable artificial intelligence model for stroke prediction using EEG signal","volume":"22","author":"Islam","year":"2022","journal-title":"Sensors"},{"issue":"13","key":"10.1016\/j.bspc.2026.110731_b23","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.3390\/s21134269","article-title":"Deep learning-based stroke disease prediction system using real-time bio signals","volume":"21","author":"Choi","year":"2021","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.bspc.2026.110731_b24","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1161\/STROKEAHA.118.024293","article-title":"Machine learning\u2013based model for prediction of outcomes in acute stroke","volume":"50","author":"Heo","year":"2019","journal-title":"Stroke"},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b25","doi-asserted-by":"crossref","first-page":"22337","DOI":"10.1038\/s41598-022-26595-z","article-title":"Multi-objective learning and explanation for stroke risk assessment in Shanxi province","volume":"12","author":"Ma","year":"2022","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.bspc.2026.110731_b26","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0213007","article-title":"Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database","volume":"14","author":"Hung","year":"2019","journal-title":"PloS One"},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s12982-024-00199-6","article-title":"Stroke recurrence prediction using machine learning and segmented neural network risk factor aggregation","volume":"21","author":"Ding","year":"2024","journal-title":"Discov. Public Health"},{"issue":"33","key":"10.1016\/j.bspc.2026.110731_b28","doi-asserted-by":"crossref","first-page":"40867","DOI":"10.1007\/s11042-025-20765-7","article-title":"A graph neural network technique for the prediction of cerebral stroke using an unbalanced medical dataset","volume":"84","author":"Sha","year":"2025","journal-title":"Multimedia Tools Appl."},{"issue":"3","key":"10.1016\/j.bspc.2026.110731_b29","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1007\/s41060-024-00597-8","article-title":"Identifying clinical feature clusters toward predicting stroke in patients with asymptomatic carotid stenosis","volume":"20","author":"Xu","year":"2025","journal-title":"Int. J. Data Sci. Anal."},{"key":"10.1016\/j.bspc.2026.110731_b30","article-title":"Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning","author":"Duan","year":"2025","journal-title":"Int. J. Nurs. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b31","doi-asserted-by":"crossref","first-page":"12718","DOI":"10.1038\/s41598-023-40036-5","article-title":"Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110731_b32","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2023.1158555","article-title":"OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features","volume":"14","author":"Ye","year":"2023","journal-title":"Front. Neurol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b33","doi-asserted-by":"crossref","first-page":"31392","DOI":"10.1038\/s41598-024-82931-5","article-title":"Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models","volume":"14","author":"Moulaei","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b34","doi-asserted-by":"crossref","first-page":"26048","DOI":"10.1038\/s41598-025-11263-9","article-title":"A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction","volume":"15","author":"El-Geneedy","year":"2025","journal-title":"Sci. Rep."},{"issue":"8","key":"10.1016\/j.bspc.2026.110731_b35","doi-asserted-by":"crossref","DOI":"10.3390\/info14080435","article-title":"Multiple explainable approaches to predict the risk of stroke using artificial intelligence","volume":"14","author":"S","year":"2023","journal-title":"Information"},{"key":"10.1016\/j.bspc.2026.110731_b36","series-title":"Kan: Kolmogorov-arnold networks","author":"Liu","year":"2024"},{"issue":"2","key":"10.1016\/j.bspc.2026.110731_b37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.bspc.2026.110731_b38","series-title":"2021 IEEE 37th International Conference on Data Engineering","first-page":"13","article-title":"Cleanml: A study for evaluating the impact of data cleaning on ml classification tasks","author":"Li","year":"2021"},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b39","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.13063\/2327-9214.1244","article-title":"A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data","volume":"4","author":"Kahn","year":"2016","journal-title":"Egems"},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b40","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s12911-019-0852-6","article-title":"A clustering approach for detecting implausible observation values in electronic health records data","volume":"19","author":"Estiri","year":"2019","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"9","key":"10.1016\/j.bspc.2026.110731_b41","first-page":"1322","article-title":"Missing data in clinical research: a tutorial on multiple imputation","volume":"37","author":"Austin","year":"2021","journal-title":"Can. J. Cardiol."},{"issue":"4","key":"10.1016\/j.bspc.2026.110731_b42","first-page":"558","article-title":"A systematic review of machine learning-based missing value imputation techniques","volume":"55","author":"Thomas","year":"2021","journal-title":"Data Technol. Appl."},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b43","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40537-020-00305-w","article-title":"Survey on categorical data for neural networks","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"issue":"1","key":"10.1016\/j.bspc.2026.110731_b44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"10.1016\/j.bspc.2026.110731_b45","series-title":"Advances on Smart and Soft Computing: Proceedings of ICACIn 2020","first-page":"37","article-title":"SMOTE\u2013ENN-based data sampling and improved dynamic ensemble selection for imbalanced medical data classification","author":"Lamari","year":"2021"},{"key":"10.1016\/j.bspc.2026.110731_b46","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing","year":"2018","journal-title":"Neural Netw."},{"issue":"2","key":"10.1016\/j.bspc.2026.110731_b47","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/78.193220","article-title":"B-spline signal processing. I. Theory","volume":"41","author":"Unser","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.bspc.2026.110731_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104306","article-title":"Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?","volume":"139","author":"Tan","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.bspc.2026.110731_b49","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"1378","article-title":"Analyzing explainer robustness via probabilistic lipschitzness of prediction functions","author":"Khan","year":"2024"},{"issue":"4","key":"10.1016\/j.bspc.2026.110731_b50","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1109\/TKDE.2023.3310909","article-title":"Multi-label clinical time-series generation via conditional GAN","volume":"36","author":"Lu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.bspc.2026.110731_b51","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2025.3646122","article-title":"Graph transformers: A survey","author":"Shehzad","year":"2026","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.bspc.2026.110731_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110301","article-title":"Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder","volume":"147","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.bspc.2026.110731_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112835","article-title":"MMSeg: A novel multi-task learning framework for class imbalance and label scarcity in medical image segmentation","volume":"309","author":"Yang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110731_b54","article-title":"Multi-sensor data fusion for smart healthcare: optimizing specialty-based classification of imbalanced EMRs","author":"Shah","year":"2025","journal-title":"Inf. Fusion"},{"issue":"4","key":"10.1016\/j.bspc.2026.110731_b55","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s10462-023-10652-8","article-title":"Cost-sensitive learning for imbalanced medical data: a review","volume":"57","author":"Araf","year":"2024","journal-title":"Artif. Intell. Rev."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012851?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012851?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:11:12Z","timestamp":1783073472000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426012851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":55,"alternative-id":["S1746809426012851"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110731","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"IGStrokeNet: An effective and explainable framework for stroke risk prediction","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110731","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110731"}}