{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T19:18:30Z","timestamp":1787685510509,"version":"build-2784847793"},"reference-count":78,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["101136244"],"award-info":[{"award-number":["101136244"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Hypertension is a major contributor to cardiovascular morbidity and mortality, yet identifying individuals at risk before clinical diagnosis remains challenging. Here, we present a multi-horizon machine learning framework designed to model incident hypertension risk across multiple clinically meaningful time windows using data from 246 286 participants in the UK Biobank. The framework systematically compares predictive performance across five horizons under severe class imbalance, enabling analysis of how discrimination, precision, and risk drivers evolve as outcome prevalence changes over time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Seven classification algorithms were evaluated, including logistic regression, random forest, na\u00efve Bayes, and four boosting-based ensemble methods. To enhance interpretability, we integrate SHapley Additive exPlanations (SHAP) with generative topographic mapping (GTM), combining feature-level attribution with population-level visualization of model predictions. Ensemble boosting models consistently achieved the strongest performance, with average precision increasing from 0.04 for the \u22642-year horizon to 0.22 for the \u226410-year horizon, while ROC-AUC remained relatively stable (\u223c0.75\u20130.79). Together, this framework reveals consistent predictors of hypertension risk (including baseline blood pressure, age, body mass index, medication burden, and cardiometabolic multimorbidity) and illustrates how combinations of risk factors organize hypertension risk across time horizons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our results demonstrate how multi-horizon modelling and complementary explainability approaches can provide deeper insight into evolving disease risk patterns in large biomedical cohorts, supporting more interpretable and scalable strategies for population-level cardiovascular prevention. Such approaches may enable earlier identification of high-risk individuals and inform targeted screening and preventive interventions in routine care settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag128","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T11:09:43Z","timestamp":1786100983000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9415-1978","authenticated-orcid":false,"given":"Akhil","family":"Naik","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Digital Technologies Research Institute, Liverpool John Moores University , Bryrom Street, Liverpool L3 3AF ,","place":["UK"]},{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5679-7501","authenticated-orcid":false,"given":"Ivan","family":"Olier","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Digital Technologies Research Institute, Liverpool John Moores University , Bryrom Street, Liverpool L3 3AF ,","place":["UK"]},{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5958-267X","authenticated-orcid":false,"given":"Ellen A","family":"Dawson","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]},{"name":"Research Institute for Sport and Exercise Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2880-5236","authenticated-orcid":false,"given":"Garry","family":"McDowell","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]},{"name":"School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5604-9378","authenticated-orcid":false,"given":"Deirdre A","family":"Lane","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]},{"name":"Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool , Derby Street, Liverpool L7 8TX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1626","authenticated-orcid":false,"given":"Gregory Y H","family":"Lip","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9927-3209","authenticated-orcid":false,"given":"Sandra","family":"Ortega-Martorell","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Digital Technologies Research Institute, Liverpool John Moores University , Bryrom Street, Liverpool L3 3AF ,","place":["UK"]},{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Derby Street , Liverpool L7 8TX ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"2026082514282511300_ztag128-B1","doi-asserted-by":"crossref","first-page":"3912","DOI":"10.1093\/eurheartj\/ehae178","article-title":"2024 ESC guidelines for the management of elevated blood pressure and hypertension","volume":"45","author":"McEvoy","year":"2024","journal-title":"Eur Heart J"},{"key":"2026082514282511300_ztag128-B2","doi-asserted-by":"crossref","first-page":"e114","DOI":"10.1161\/CIR.0000000000001396","article-title":"2025 AHA\/ACC\/AANP\/AAPA\/ABC\/ACCP\/ACPM\/AGS\/AMA\/ASPC\/NMA\/PCNA\/SGIM guideline for the prevention, detection, evaluation and management of high blood pressure in adults: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"152","author":"Jones","year":"2025","journal-title":"Circulation"},{"key":"2026082514282511300_ztag128-B3","volume-title":"Global Report on Hypertension: the Race Against a Silent Killer","author":"World Health Organization"},{"key":"2026082514282511300_ztag128-B4","author":"World Health Organization"},{"key":"2026082514282511300_ztag128-B5"},{"key":"2026082514282511300_ztag128-B6","doi-asserted-by":"publisher","author":"Fryar","DOI":"10.15620\/cdc\/164016"},{"key":"2026082514282511300_ztag128-B7","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"key":"2026082514282511300_ztag128-B8","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0187240","article-title":"Recent development of risk-prediction models for incident hypertension: an updated systematic review","volume":"12","author":"Sun","year":"2017","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B9","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0266334","article-title":"Prediction of hypertension using traditional regression and machine learning models: a systematic review and meta-analysis","volume":"17","author":"Chowdhury","year":"2022","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B10","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0067370","article-title":"Risk models to predict hypertension: a systematic review","volume":"8","author":"Echouffo-Tcheugui","year":"2013","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B11","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0294148","article-title":"Prognostic risk models for incident hypertension: a PRISMA systematic review and meta-analysis","volume":"19","author":"Schjerven","year":"2024","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B12","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.amjmed.2008.10.034","article-title":"Prediction of incident hypertension risk in women with currently normal blood pressure","volume":"122","author":"Paynter","year":"2009","journal-title":"Am J Med"},{"key":"2026082514282511300_ztag128-B13","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1161\/HYPERTENSIONAHA.112.202655","article-title":"Prediction of blood pressure changes over time and incidence of hypertension by a genetic risk score in Swedes","volume":"61","author":"Fava","year":"2013","journal-title":"Hypertension"},{"key":"2026082514282511300_ztag128-B14","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1038\/jhh.2016.23","article-title":"Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study","volume":"30","author":"Chen","year":"2016","journal-title":"J Hum Hypertens"},{"key":"2026082514282511300_ztag128-B15","doi-asserted-by":"crossref","first-page":"102","DOI":"10.7326\/0003-4819-148-2-200801150-00005","article-title":"A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study","volume":"148","author":"Parikh","year":"2008","journal-title":"Ann Intern Med"},{"key":"2026082514282511300_ztag128-B16","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1161\/HYPERTENSIONAHA.109.132373","article-title":"Validating the framingham hypertension risk score: results from the whitehall II study","volume":"54","author":"Kivim\u00e4ki","year":"2009","journal-title":"Hypertension"},{"key":"2026082514282511300_ztag128-B17","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1186\/s12889-021-10760-6","article-title":"Validation of the framingham hypertension risk score in a middle eastern population: Tehran lipid and glucose study (TLGS)","volume":"21","author":"Koohi","year":"2021","journal-title":"BMC Public Health"},{"key":"2026082514282511300_ztag128-B18","first-page":"455","article-title":"Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment","volume":"56","author":"Rajula","year":"2020","journal-title":"Medicina (Lithuania)"},{"key":"2026082514282511300_ztag128-B19","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2021.619429","article-title":"Predicting the risk of hypertension based on several easy-to-collect risk factors: a machine learning method","volume":"9","author":"Zhao","year":"2021","journal-title":"Front Public Health"},{"key":"2026082514282511300_ztag128-B20","doi-asserted-by":"crossref","first-page":"14487","DOI":"10.1007\/s00521-021-06060-0","article-title":"A hybrid machine learning approach for hypertension risk prediction","volume":"35","author":"Fang","year":"2023","journal-title":"Neural Comput Appl"},{"key":"2026082514282511300_ztag128-B21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41598-022-27264-x","article-title":"A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population","volume":"13","author":"Chowdhury","year":"2023","journal-title":"Sci Rep"},{"key":"2026082514282511300_ztag128-B22","doi-asserted-by":"crossref","DOI":"10.1093\/jamiaopen\/ooac097","article-title":"Predicting hypertension onset from longitudinal electronic health records with deep learning","volume":"5","author":"Datta","year":"2022","journal-title":"JAMIA Open"},{"key":"2026082514282511300_ztag128-B23","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1038\/s41440-021-00738-7","article-title":"Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China","volume":"44","author":"Niu","year":"2021","journal-title":"Hypertens Res"},{"key":"2026082514282511300_ztag128-B24","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1111\/jch.13759","article-title":"Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques","volume":"22","author":"Kanegae","year":"2020","journal-title":"J Clin Hypertens"},{"key":"2026082514282511300_ztag128-B25","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.1038\/s41598-024-56170-7","article-title":"Development of risk models of incident hypertension using machine learning on the HUNT study data","volume":"14","author":"Schjerven","year":"2024","journal-title":"Sci Rep"},{"key":"2026082514282511300_ztag128-B26","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"2026082514282511300_ztag128-B27","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011396","article-title":"Personalized prediction for multiple chronic diseases by developing the multi-task cox learning model","volume":"19","author":"Zhang","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2026082514282511300_ztag128-B28","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3294-8","volume-title":"Modeling Survival Data: Extending the Cox Model","author":"Therneau","year":"2000"},{"key":"2026082514282511300_ztag128-B29","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110718","article-title":"Artificial intelligence and digital twins for the personalised prediction of hypertension risk","volume":"196","author":"Naik","year":"2025","journal-title":"Comput Biol Med"},{"key":"2026082514282511300_ztag128-B30","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/s41379-021-00891-x","article-title":"Limitations of multivariate survival analysis","volume":"35","author":"Ramia de Cap","year":"2022","journal-title":"Mod Pathol"},{"key":"2026082514282511300_ztag128-B31","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102813","article-title":"A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data","volume":"68","author":"Martinez-R\u00edos","year":"2021","journal-title":"Biomed Signal Process Control"},{"key":"2026082514282511300_ztag128-B32","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1186\/s12911-019-0874-0","article-title":"On the interpretability of machine learning-based model for predicting hypertension","volume":"19","author":"Elshawi","year":"2019","journal-title":"BMC Med Inform Decis Mak"},{"key":"2026082514282511300_ztag128-B33","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0315865","article-title":"Optimizing hypertension prediction using ensemble learning approaches","volume":"19","author":"Sifat","year":"2024","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B34","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0289613","article-title":"Predicting the risk of hypertension using machine learning algorithms: a cross sectional study in Ethiopia","volume":"18","author":"Islam","year":"2023","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B35","author":"Lundberg"},{"key":"2026082514282511300_ztag128-B36","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1162\/089976698300017953","article-title":"The generative topographic mapping","volume":"10","author":"Bishop","year":"1998","journal-title":"Neural Comput"},{"key":"2026082514282511300_ztag128-B37","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS One"},{"key":"2026082514282511300_ztag128-B38","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/08037050410004819","article-title":"Prediction of future hypertension by casual blood pressure or invasive hemodynamics? A 30-year follow-up study","volume":"13","author":"Andersson","year":"2004","journal-title":"Blood Press"},{"key":"2026082514282511300_ztag128-B39","article-title":"Association between high blood pressure and long term cardiovascular events in young adults: systematic review and meta-analysis","volume":"370","author":"Luo","year":"2020","journal-title":"BMJ"},{"key":"2026082514282511300_ztag128-B40","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1097\/00126097-200212000-00001","article-title":"What is the white-coat effect and how should it be measured?","volume":"7","author":"Pickering","year":"2002","journal-title":"Blood Press Monit"},{"key":"2026082514282511300_ztag128-B41","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1161\/CIRCULATIONAHA.112.117333","article-title":"Body mass Index and risk of incident hypertension over the life course","volume":"126","author":"Shihab","year":"2012","journal-title":"Circulation"},{"key":"2026082514282511300_ztag128-B42","doi-asserted-by":"crossref","first-page":"97","DOI":"10.5334\/gh.1487","article-title":"Body Mass Index and the risk of hypertension-diabetes comorbidity in elderly population: a prospective cohort in China","volume":"20","author":"Zhang","year":"2025","journal-title":"Glob Heart"},{"key":"2026082514282511300_ztag128-B43","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1097\/HJH.0000000000002788","article-title":"The association between self-perceived walking pace with the incidence of hypertension: the \u2018Seguimiento Universidad de Navarra\u2019 cohort","volume":"39","author":"Etzig","year":"2021","journal-title":"J Hypertens"},{"key":"2026082514282511300_ztag128-B44","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-78541-w","article-title":"Association between subjective walking speed and metabolic diseases in individuals with obesity: a cross-sectional analysis","volume":"14","author":"Yamamoto","year":"2024","journal-title":"Sci Rep"},{"key":"2026082514282511300_ztag128-B45","first-page":"575","article-title":"Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms","volume":"34","author":"Petrie","year":"2018","journal-title":"Can J Cardiol"},{"key":"2026082514282511300_ztag128-B46","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.115.003053","article-title":"Dyslipidemia and the risk of developing hypertension in a working-age male population","volume":"5","author":"Otsuka","year":"2016","journal-title":"J Am Heart Assoc"},{"key":"2026082514282511300_ztag128-B47","first-page":"281","article-title":"Correlation of apolipoprotein A1 and apolipoprotein B100 with systolic blood pressure in essential hypertensive subjects","volume":"15","author":"Kumar Shah","year":"2025","journal-title":"J Chem Health Risks"},{"key":"2026082514282511300_ztag128-B48","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1186\/s12889-022-12799-5","article-title":"Association of socioeconomic status with hypertension prevalence and control in Nanjing: a cross-sectional study","volume":"22","author":"Qin","year":"2022","journal-title":"BMC Public Health"},{"key":"2026082514282511300_ztag128-B49","article-title":"A review of deep learning based anomaly detection","volume":"668","author":"Shi","year":"2026","journal-title":"Neurocomputing (Amst)"},{"key":"2026082514282511300_ztag128-B50","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103517","article-title":"Foundation models and transformers for anomaly detection: a survey","volume":"126","author":"Ammar","year":"2026","journal-title":"Inf Fusion"},{"key":"2026082514282511300_ztag128-B51","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolmodel.2023.110414","article-title":"Correcting for the effects of class imbalance improves the performance of machine-learning based species distribution models","volume":"483","author":"Benkendorf","year":"2023","journal-title":"Ecol Modell"},{"key":"2026082514282511300_ztag128-B52","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1013211","article-title":"Identifying predictors and assessing causal effect on hypertension risk among adults using double machine learning models: insights from Bangladesh Demographic and Health Survey","volume":"21","author":"Ghosh","year":"2025","journal-title":"PLoS Comput Biol"},{"key":"2026082514282511300_ztag128-B53","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann Intern Med"},{"key":"2026082514282511300_ztag128-B54","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s12916-019-1339-0","article-title":"Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study","volume":"17","author":"Chudasama","year":"2019","journal-title":"BMC Med"},{"key":"2026082514282511300_ztag128-B55","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1161\/HYPERTENSIONAHA.119.14240","article-title":"High blood pressure and cardiovascular disease","volume":"75","author":"Fuchs","year":"2020","journal-title":"Hypertension"},{"key":"2026082514282511300_ztag128-B56","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.3892\/etm.2016.3667","article-title":"Obesity and hypertension","volume":"12","author":"Jiang","year":"2016","journal-title":"Exp Ther Med"},{"key":"2026082514282511300_ztag128-B57","doi-asserted-by":"crossref","first-page":"3149","DOI":"10.2147\/OPTH.S408171","article-title":"Visual impairment and its associated factors among hypertensive patients in amhara region referral hospitals, Ethiopia","volume":"17","author":"Ashenef","year":"2023","journal-title":"Clinical Ophthalmology"},{"key":"2026082514282511300_ztag128-B58"},{"key":"2026082514282511300_ztag128-B59","first-page":"e596","article-title":"2019 ACC\/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology\/American Heart Association Task Force on Clinical Practice Guidelines","volume":"140","author":"Arnett","year":"2019","journal-title":"Circulation"},{"key":"2026082514282511300_ztag128-B60","doi-asserted-by":"crossref","first-page":"486","DOI":"10.5812\/ijem.3505","article-title":"Normality tests for statistical analysis: a guide for non-statisticians","volume":"10","author":"Ghasemi","year":"2012","journal-title":"Int J Endocrinol Metab"},{"key":"2026082514282511300_ztag128-B61","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","article-title":"User\u2019s guide to correlation coefficients","volume":"18","author":"Akoglu","year":"2018","journal-title":"Turk J Emerg Med"},{"key":"2026082514282511300_ztag128-B62","doi-asserted-by":"crossref","first-page":"S184","DOI":"10.5005\/jp-journals-10071-23815","article-title":"An Introduction to statistics: choosing the correct statistical test","volume":"25","author":"Ranganathan","year":"2021","journal-title":"Indian J Crit Care Med"},{"key":"2026082514282511300_ztag128-B63","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1111\/opo.12131","article-title":"When to use the Bonferroni correction","volume":"34","author":"Armstrong","year":"2014","journal-title":"Ophthalmic Physiol Opt"},{"key":"2026082514282511300_ztag128-B64","doi-asserted-by":"crossref","first-page":"12","DOI":"10.11613\/BM.2014.003","article-title":"Understanding logistic regression analysis","volume":"24","author":"Sperandei","year":"2014","journal-title":"Biochem Med (Zagreb)"},{"key":"2026082514282511300_ztag128-B65","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/978-0-387-30164-8_576","volume-title":"Encyclopedia of Machine Learning","author":"Webb","year":"2011"},{"key":"2026082514282511300_ztag128-B66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2026082514282511300_ztag128-B67","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front Neurorobot"},{"key":"2026082514282511300_ztag128-B68","author":"Chen"},{"key":"2026082514282511300_ztag128-B69","author":"Prokhorenkova"},{"key":"2026082514282511300_ztag128-B70","author":"Ke"},{"key":"2026082514282511300_ztag128-B71","author":"Akiba"},{"key":"2026082514282511300_ztag128-B72","first-page":"223","article-title":"A Gaussian-Bernoulli mixed na\u00efve Bayes approach to predict students\u2019 academic procrastination tendencies in online mathematics learning","volume":"10","author":"Darwin Godinez St Rita","year":"2022","journal-title":"Am J Educ Res"},{"key":"2026082514282511300_ztag128-B73","doi-asserted-by":"crossref","first-page":"6086","DOI":"10.1038\/s41598-024-56706-x","article-title":"Evaluation metrics and statistical tests for machine learning","volume":"14","author":"Rainio","year":"2024","journal-title":"Sci Rep"},{"key":"2026082514282511300_ztag128-B74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/CBO9780511528446","volume-title":"The Shapley Value","author":"Roth","year":"1988"},{"key":"2026082514282511300_ztag128-B75","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2026082514282511300_ztag128-B76","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.neunet.2008.05.013","article-title":"Advances in clustering and visualization of time series using GTM through time","volume":"21","author":"Olier","year":"2008","journal-title":"Neural Networks"},{"key":"2026082514282511300_ztag128-B77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-023-05554-x","article-title":"Mapping the global free expression landscape using machine learning","volume":"5","author":"Ortega-Martorell","year":"2023","journal-title":"SN Appl Sci"},{"key":"2026082514282511300_ztag128-B78","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2024.105280","article-title":"AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations","volume":"107","author":"Bellfield","year":"2024","journal-title":"EBioMedicine"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztag128\/70603334\/ztag128.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/7\/ztag128\/70603334\/ztag128.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/7\/ztag128\/70603334\/ztag128.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T18:28:47Z","timestamp":1787682527000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/doi\/10.1093\/ehjdh\/ztag128\/8758363"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":78,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7,29]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztag128","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,8]]},"published":{"date-parts":[[2026,7,29]]},"article-number":"ztag128"}}