{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:26:31Z","timestamp":1780676791331,"version":"3.54.1"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Mild cognitive impairment (MCI) is considered a preclinical stage of Alzheimer\u2019s disease (AD). People with MCI have a higher risk of developing dementia than healthy people. As one of the risk factors for MCI, stroke has been actively treated and intervened. Therefore, selecting the high-risk population of stroke as the research object and discovering the risk factors of MCI as early as possible can prevent the occurrence of MCI more effectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The Boruta algorithm was used to screen variables, and eight machine learning models were established and evaluated. The best performing models were used to assess variable importance and build an online risk calculator. Shapley additive explanation is used to explain the model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 199 patients were included in the study, 99 of whom were male. Transient ischemic attack (TIA), homocysteine, education, hematocrit (HCT), diabetes, hemoglobin, red blood cells (RBC), hypertension, prothrombin time (PT) were selected by Boruta algorithm. Logistic regression (AUC = 0.8595) was the best model for predicting MCI in high-risk groups of stroke, followed by elastic network (ENET) (AUC = 0.8312), multilayer perceptron (MLP) (AUC = 0.7908), extreme gradient boosting (XGBoost) (AUC = 0.7691), and support vector machine (SVM) (AUC = 0.7527), random forest (RF) (AUC = 0.7451), K-nearest neighbors (KNN) (AUC = 0.7380), decision tree (DT) (AUC = 0.6972). The importance of variables suggests that TIA, diabetes, education, and hypertension are the top four variables of importance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Transient ischemic attack (TIA), diabetes, education, and hypertension are the most important risk factors for MCI in high-risk groups of stroke, and early intervention should be performed to reduce the occurrence of MCI.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2023.1180351","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T05:17:10Z","timestamp":1686806230000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Development and validation of an interpretable machine learning model\u2014Predicting mild cognitive impairment in a high-risk stroke population"],"prefix":"10.3389","volume":"15","author":[{"given":"Feng-Juan","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xie-Hui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Qing","family":"Quan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Yi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Liang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/s0022-510x(02)00261-7","article-title":"Natural history of dementia associated with lacunar infarctions.","author":"Aharon-Peretz","year":"2002","journal-title":"J. Neurol. Sci"},{"key":"B2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1136\/jnnp.2005.082313","article-title":"Vascular cognitive impairment.","volume":"76","author":"Bowler","year":"2005","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"B3","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1111\/j.1445-5994.2012.02758.x","article-title":"Diabetes as a risk factor for dementia and mild cognitive impairment: A meta-analysis of longitudinal studies.","volume":"42","author":"Cheng","year":"2012","journal-title":"Intern. Med. J."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.c3666","article-title":"The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis.","volume":"341","author":"Debette","year":"2010","journal-title":"BMJ"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2670","DOI":"10.1161\/01.STR.0000189626.71033.35","article-title":"Evolution of cognitive impairment after stroke and risk factors for delayed progression.","volume":"36","author":"del Ser","year":"2005","journal-title":"Stroke"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1161\/01.str.27.10.1798","article-title":"Recovery of cognitive function after stroke.","volume":"27","author":"Desmond","year":"1996","journal-title":"Stroke"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e661","DOI":"10.1016\/S2468-2667(20)30185-7","article-title":"Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study.","volume":"5","author":"Jia","year":"2020","journal-title":"Lancet Public Health"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-018-0951-8","article-title":"Montreal cognitive assessment reflects cognitive reserve.","volume":"18","author":"Kang","year":"2018","journal-title":"BMC Geriatr."},{"key":"B9","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1097\/ALN.0000000000002374","article-title":"Supervised machine-learning predictive analytics for prediction of postinduction hypotension.","volume":"129","author":"Kendale","year":"2018","journal-title":"Anesthesiology"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-018-1971-2","article-title":"Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: A retrospective cohort study.","volume":"18","author":"Kuhle","year":"2018","journal-title":"BMC Pregnancy Childbirth"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the boruta package.","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"B12","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1097\/ALN.0000000000001892","article-title":"Prediction of bispectral index during target-controlled infusion of propofol and remifentanil: A deep learning approach.","volume":"128","author":"Lee","year":"2018","journal-title":"Anesthesiology"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2021.719352","article-title":"Risk identification of bronchopulmonary dysplasia in premature infants based on machine learning.","volume":"9","author":"Lei","year":"2021","journal-title":"Front. Pediatr."},{"key":"B14","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1097\/CM9.0000000000000694","article-title":"Machine learning in nephrology: Scratching the surface.","volume":"133","author":"Li","year":"2020","journal-title":"Chin. Med. J."},{"key":"B15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jalz.2018.06.3060","article-title":"Prediabetes and diabetes accelerate cognitive decline and predict microvascular lesions: A population-based cohort study.","volume":"15","author":"Marseglia","year":"2019","journal-title":"Alzheimers Dement."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1136\/jnnp.2007.142679","article-title":"Temporal trends in the long term risk of progression of mild cognitive impairment: A pooled analysis.","volume":"79","author":"Mitchell","year":"2008","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"B17","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1212\/wnl.58.1.56","article-title":"Cognitive and functional decline in African Americans with VaD, AD, and stroke without dementia.","volume":"58","author":"Nyenhuis","year":"2002","journal-title":"Neurology"},{"key":"B18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/s1474-4422(03)00305-3","article-title":"Vascular cognitive impairment.","volume":"2","author":"O\u2019Brien","year":"2003","journal-title":"Lancet Neurol."},{"key":"B19","doi-asserted-by":"publisher","first-page":"3116","DOI":"10.1161\/STROKEAHA.111.621490","article-title":"Transient cognitive impairment in TIA and minor stroke.","volume":"42","author":"Pendlebury","year":"2011","journal-title":"Stroke"},{"key":"B20","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1212\/CON.0000000000000313","article-title":"Mild cognitive impairment.","volume":"22","author":"Petersen","year":"2016","journal-title":"Continuum"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1001\/archneurol.2009.266","article-title":"Mild cognitive impairment: Ten years later.","volume":"66","author":"Petersen","year":"2009","journal-title":"Arch. Neurol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1001\/archneur.56.3.303","article-title":"Mild cognitive impairment: Clinical characterization and outcome.","volume":"56","author":"Petersen","year":"1999","journal-title":"Arch. Neurol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1001\/archneur.58.12.1985","article-title":"Current concepts in mild cognitive impairment.","volume":"58","author":"Petersen","year":"2001","journal-title":"Arch. Neurol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"427","DOI":"10.7326\/0003-4819-148-6-200803180-00005","article-title":"Prevalence of cognitive impairment without dementia in the United States.","volume":"148","author":"Plassman","year":"2008","journal-title":"Ann. Intern. Med."},{"key":"B25","doi-asserted-by":"publisher","first-page":"169","DOI":"10.2337\/dc08-0272","article-title":"Metabolic syndrome and risk for incident Alzheimer\u2019s disease or vascular dementia: The Three-City Study.","volume":"32","author":"Raffaitin","year":"2009","journal-title":"Diabetes Care"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24021659","article-title":"Rationale for a multi-factorial approach for the reversal of cognitive decline in Alzheimer\u2019s Disease and MCI: A review.","volume":"24","author":"Rao","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"B27","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1001\/archneur.64.1.93","article-title":"The metabolic syndrome and Alzheimer disease.","volume":"64","author":"Razay","year":"2007","journal-title":"Arch. Neurol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1159\/000091434","article-title":"Clinical determinants of dementia and mild cognitive impairment following ischaemic stroke: The Sydney Stroke Study.","volume":"21","author":"Sachdev","year":"2006","journal-title":"Dement. Geriatr. Cogn. Disord"},{"key":"B29","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1002\/dmrr.2664","article-title":"Memory and executive functions in persons with type 2 diabetes: A meta-analysis.","volume":"32","author":"Sadanand","year":"2016","journal-title":"Diabetes Metab. Res. Rev."},{"key":"B30","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1111\/j.1747-4949.2011.00731.x","article-title":"Dementia, stroke, and vascular risk factors; a review.","volume":"7","author":"Sahathevan","year":"2012","journal-title":"Int. J. Stroke"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1016\/s0140-6736(96)90608-x","article-title":"15-year longitudinal study of blood pressure and dementia","volume":"347","author":"Skoog","year":"1996","journal-title":"Lancet"},{"key":"B32","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1136\/jnnp.2009.181743","article-title":"Metabolic syndrome and the risk of vascular dementia: The Italian longitudinal study on ageing.","volume":"81","author":"Solfrizzi","year":"2010","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"B33","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1212\/01.wnl.0000137042.01774.33","article-title":"Progressive dementia after first-ever stroke: A community-based follow-up study.","volume":"63","author":"Srikanth","year":"2004","journal-title":"Neurology"},{"key":"B34","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.cger.2018.06.005","article-title":"Mild cognitive impairment in geriatrics.","volume":"34","author":"Tangalos","year":"2018","journal-title":"Clin. Geriatr. Med."},{"key":"B35","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/s0022-510x(02)00260-5","article-title":"Progression of cognitive impairment after stroke: One year results from a longitudinal study of Singaporean stroke patients.","volume":"20","author":"Tham","year":"2002","journal-title":"J. Neurol. Sci"},{"key":"B36","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1002\/brb3.88","article-title":"Mild cognitive impairment: Effect of education on the verbal and nonverbal tasks performance decline.","volume":"2","author":"Vadikolias","year":"2012","journal-title":"Brain Behav."},{"key":"B37","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1212\/01.wnl.0000234037.91185.99","article-title":"Association of metabolic syndrome with Alzheimer disease: A population-based study.","volume":"67","author":"Vanhanen","year":"2006","journal-title":"Neurology"},{"key":"B38","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1212\/01.wnl.0000341271.90478.8e","article-title":"Vascular risk factors and dementia: How to move forward?","volume":"72","author":"Viswanathan","year":"2009","journal-title":"Neurology"},{"key":"B39","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s11102-020-01056-w","article-title":"Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery.","volume":"23","author":"Voglis","year":"2020","journal-title":"Pituitary"},{"key":"B40","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1212\/wnl.57.4.714","article-title":"Progression of impairment in patients with vascular cognitive impairment without dementia.","volume":"57","author":"Wentzel","year":"2001","journal-title":"Neurology"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2019.100944","article-title":"Diabetes mellitus and risks of cognitive impairment and dementia: A systematic review and meta-analysis of 144 prospective studies.","volume":"55","author":"Xue","year":"2019","journal-title":"Ageing Res. Rev."},{"key":"B42","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1159\/000095427","article-title":"Subtype of mild cognitive impairment and progression to dementia and death.","volume":"22","author":"Yaffe","year":"2006","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"B43","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.diabres.2016.10.024","article-title":"An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer\u2019s disease.","volume":"124","author":"Zhang","year":"2017","journal-title":"Diabetes Res. Clin. Pract."},{"key":"B44","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.neulet.2009.12.057","article-title":"Impairments in cognition and resting-state connectivity of the hippocampus in elderly subjects with type 2 diabetes.","volume":"473","author":"Zhou","year":"2010","journal-title":"Neurosci. Lett."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2023.1180351\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T05:17:14Z","timestamp":1686806234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2023.1180351\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,15]]},"references-count":44,"alternative-id":["10.3389\/fnagi.2023.1180351"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2023.1180351","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,15]]},"article-number":"1180351"}}