{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:27:58Z","timestamp":1767857278083,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Our study focuses on predicting stroke in a general context rather than specific subtypes. This clarification will not only ensure a clear understanding of our study\u2019s scope but also enhance the overall transparency and impact of our findings. We construct an optimization model and describe an effective methodology and algorithms for machine learning classification, accommodating missing data and imbalances. Our models outperform previous efforts in stroke prediction, demonstrating higher sensitivity, specificity, accuracy, and precision. Data quality and preprocessing play a crucial role in developing reliable models. The proposed algorithm using SVMs achieves 98% accuracy and 97% recall score. In-depth data analysis and advanced machine learning techniques improve stroke prediction. This research highlights the value of data-oriented approaches, leading to enhanced accuracy and understanding of stroke risk factors. These methods can be applied to other medical domains, benefiting patient care and public health outcomes. By incorporating our findings, the efficiency and effectiveness of the public health system can be improved.<\/jats:p>","DOI":"10.3390\/a16090417","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T08:45:31Z","timestamp":1693557931000},"page":"417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Optimization Precise Model of Stroke Data to Improve Stroke Prediction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9019-072X","authenticated-orcid":false,"given":"Ivan G.","family":"Ivanov","sequence":"first","affiliation":[{"name":"Faculty of Economics and Business Administration, Sofia University \u201cSt.Kl.Ohridski\u201d, 1000 Sofia, Bulgaria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8419-4410","authenticated-orcid":false,"given":"Yordan","family":"Kumchev","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Social Sciences, Plovdiv University Paisii Hilendarski, 4000 Plovdiv, Bulgaria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6473-700X","authenticated-orcid":false,"given":"Vincent James","family":"Hooper","sequence":"additional","affiliation":[{"name":"College of Business Administration, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"ref_1","first-page":"74","article-title":"Primary stroke prevention worldwide: Translating evidence into action","volume":"7","author":"Owolabi","year":"2022","journal-title":"Health Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1097\/CCM.0000000000004597","article-title":"Management of Acute Ischemic Stroke","volume":"48","author":"Herpich","year":"2020","journal-title":"Crit. Care Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100085","DOI":"10.1016\/j.ajpc.2020.100085","article-title":"Stroke in young adults: Current trends, opportunities for prevention and pathways forward","volume":"3","author":"Yahya","year":"2020","journal-title":"Am. J. Prev. Cardiol."},{"key":"ref_4","unstructured":"Kaggle (2023, March 30). Stroke Prediction Dataset. Available online: https:\/\/www.kaggle.com\/fedesoriano\/stroke-prediction-dataset\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1212\/WNL.0b013e318259e221","article-title":"An integer-based score to predict functional outcome in acute ischemic stroke: The ASTRAL score","volume":"78","author":"Ntaios","year":"2012","journal-title":"Neurology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1177\/2396987318754591","article-title":"Development and validation of the Dutch Stroke Score for predicting disability and functional outcome after ischemic stroke: A tool to support efficient discharge planning","volume":"3","author":"Dijkland","year":"2018","journal-title":"Eur. Stroke J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1111\/ene.13100","article-title":"ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians","volume":"23","author":"Ntaios","year":"2016","journal-title":"Eur. J. Neurol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"837650","DOI":"10.3389\/fncel.2022.837650","article-title":"Migraine Aura, Transient Ischemic Attacks, Stroke, and Dying of the Brain Share the Same Key Pathophysiological Process in Neurons Driven by Gibbs\u2013Donnan Forces. Namely Spreading Depolarization","volume":"16","author":"Lemale","year":"2022","journal-title":"Front. Cell. Neurosci."},{"key":"ref_9","first-page":"2642458","article-title":"Prevalence of Stroke and Associated Risk Factors in Sleman District of Yogyakarta Special Region Indonesia","volume":"2019","author":"Setyopranoto","year":"2019","journal-title":"Stroke Res. Treat."},{"key":"ref_10","unstructured":"World Stroke Organization (2023, March 30). Learn about Stroke. Available online: https:\/\/www.world-stroke.org\/world-stroke-day-campaign\/why-stroke-matters\/learn-about-stroke."},{"key":"ref_11","unstructured":"World Stroke Organization (2023, March 30). Global Stroke Fact Sheet 2022. Available online: https:\/\/www.world-stroke.org\/assets\/downloads\/WSO_Global_Stroke_Fact_Sheet.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tyagi, A.K. (2021). Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications, Scrivener Publishing LLC. Chapter 18.","DOI":"10.1002\/9781119785750"},{"key":"ref_13","unstructured":"Machine Learning (2023, March 30). Imbalanced Data. Available online: https:\/\/developers.google.com\/machine-learning\/data-prep\/construct\/sampling-splitting\/imbalanced-data."},{"key":"ref_14","unstructured":"(2023, March 30). Documentation for Random Forest Classification from Scikitlearn. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html."},{"key":"ref_15","unstructured":"(2023, March 30). Documentation for Decision Tree Classification from Scikitlearn. Available online: https:\/\/scikit-learn.org\/stable\/modules\/tree.html."},{"key":"ref_16","unstructured":"(2023, March 30). Documentation for Support Vector Machines (SVMs) from Scikitlearn. Available online: https:\/\/scikit-learn.org\/stable\/modules\/svm.html."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7633381","DOI":"10.1155\/2021\/7633381","article-title":"Stroke Disease Detection and Prediction Using Robust Learning Approaches","volume":"2021","author":"Tazin","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_18","first-page":"539","article-title":"Analyzing the performance of stroke prediction using ML classification algorithms","volume":"12","author":"Sailasya","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dritsas, E., and Trigka, M. (2022). Stroke Risk Prediction with Machine Learning Techniques. Sensors, 22.","DOI":"10.3390\/s22134670"},{"key":"ref_20","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."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"51","DOI":"10.2174\/1570159X17666190726104139","article-title":"Pharmacological Interventions and Rehabilitation Approach for Enhancing Brain Self-repair and Stroke Recovery","volume":"18","author":"Szelenberger","year":"2020","journal-title":"Curr. Neuropharmacol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1161\/STROKEAHA.120.030427","article-title":"Interventions Targeting Racial\/Ethnic Disparities in Stroke Prevention and Treatment","volume":"51","author":"Levine","year":"2020","journal-title":"Stroke"},{"key":"ref_23","first-page":"100550","article-title":"Prevalence of stroke in China, 2013\u20132019: A population-based study","volume":"28","author":"Tu","year":"2022","journal-title":"Lancet Reg. Health\u2014West. Pac."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1089\/tmj.2020.0019","article-title":"Impact of Telerehabilitation for Stroke-Related Deficits","volume":"27","author":"Knepley","year":"2021","journal-title":"Telemed. e-Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40537-020-00390-x","article-title":"Resampling imbalanced data for network intrusion detection datasets","volume":"8","author":"Bagui","year":"2021","journal-title":"J. Big Data"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shahbazian, R., and Trubitsyna, I. (2022). DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation. Information, 13.","DOI":"10.3390\/info13120575"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103465","DOI":"10.1016\/j.jbi.2020.103465","article-title":"A hybrid sampling algorithm combining M-SMOTE and ENN based on random forest for medical imbalanced data","volume":"107","author":"Xu","year":"2020","journal-title":"J. Biomed. 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