This paper is in the following e-collection/theme issue:

Machine Learning (2006) Stroke (229) Big Data (306) Clinical Information and Decision Making (1842) Electronic Health Records (1282) Bioinformatics Methods and Algorithms (16) Hypertension Prevention and Treatment (287)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30277, first published .
Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study

Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study

Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study

Authors of this article:

Yujie Yang1, 2 Author Orcid Image ;   Jing Zheng3 Author Orcid Image ;   Zhenzhen Du1 Author Orcid Image ;   Ye Li1, 4 Author Orcid Image ;   Yunpeng Cai1 Author Orcid Image
Article Authors Cited by (22) Tweetations (4) Metrics

Journals

  1. Mapundu M, Kabudula C, Musenge E, Olago V, Celik T. Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa. Frontiers in Public Health 2022;10 View
  2. Zhao P, Xue J, Xu X, Wang L, Chen D, Wong K. Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation. Computational and Mathematical Methods in Medicine 2022;2022:1 View
  3. Liao X, Yao C, Zhang J, Liu L. Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review. Journal of Evidence-Based Medicine 2023;16(4):534 View
  4. Saleem M, Javeed A, Akarathanawat W, Chutinet A, Suwanwela N, Asdornwised W, Chaitusaney S, Deelertpaiboon S, Srisiri W, Benjapolakul W, Kaewplung P. Innovations in Stroke Identification: A Machine Learning-Based Diagnostic Model Using Neuroimages. IEEE Access 2024;12:35754 View
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  7. Islam U, Mehmood G, Al-Atawi A, Khan F, Alwageed H, Cascone L. NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics. Journal of Neuroscience Methods 2024;409:110210 View
  8. Li X, Wang S, Wu K, Mo C, Li F, Cheng Z, Liang F, Zheng J, Gu D. Time-dependent cardiovascular risks following pneumonia in inpatient and outpatient settings: A register-based cohort study. International Journal of Cardiology Cardiovascular Risk and Prevention 2024;22:200317 View
  9. Gupta A, Mishra N, Jatana N, Malik S, Gepreel K, Asmat F, Mohanty S. Predicting stroke risk: An effective stroke prediction model based on neural networks. Journal of Neurorestoratology 2025;13(1):100156 View
  10. Asadi F, Rahimi M, Daeechini A, Paghe A. The most efficient machine learning algorithms in stroke prediction: A systematic review. Health Science Reports 2024;7(10) View
  11. Du Z, Wang S, Yang O, He J, Yang Y, Zheng J, Zhao H, Cai Y. Machine-learning-based prediction of cardiovascular events for hyperlipidemia population with lipid variability and remnant cholesterol as biomarkers. Health Information Science and Systems 2024;12(1) View
  12. Soladoye A, Olawade D, Adeyanju I, Akpa O, Aderinto N, Owolabi M. Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa. Clinical Neurology and Neurosurgery 2025;249:108761 View
  13. Heseltine-Carp W, Courtman M, Browning D, Kasabe A, Allen M, Streeter A, Ifeachor E, James M, Mullin S. Machine learning to predict stroke risk from routine hospital data: A systematic review. International Journal of Medical Informatics 2025;196:105811 View
  14. Miao S, Ji P, Zhu Y, Meng H, Jing M, Sheng R, Zhang X, Ding H, Guo J, Gao W, Yang G, Liu Y. The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study. JMIR Medical Informatics 2025;13:e63186 View
  15. Huang J, Liu W. Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model. Medicine 2025;104(22):e42690 View
  16. Argade V, Gautam D, Singh D, Vishwakarma R. Intelligent Neuroimage-based Stroke Diagnosis Using Deep Learning and Dual Web Interface for Clinical Use. Journal of Artificial Intelligence and Capsule Networks 2025;7(2):191 View
  17. Soladoye A, Aderinto N, Popoola M, Adeyanju I, Osonuga A, Olawade D. Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps. International Journal of Medical Informatics 2025;203:106041 View

Conference Proceedings

  1. Saleh H, Hussien A, Hassan M, Ali A. 2022 International Conference on Engineering & MIS (ICEMIS). Predicting Stroke Disease Based on Recurrent Neural Network and Optimization techniques View
  2. Prathima C, Moni M, Charitha M, Nandish B, Waseemuddin M, Reddy K. 2025 3rd International Conference on Disruptive Technologies (ICDT). A Novel Approach for Early Brain Stroke Detection Using Genetic Algorithm View
  3. Basu S, Dey S, Ganguly P, Basak D, Banerjee P, Chakrabarti A. 2025 Devices for Integrated Circuit (DevIC). Comparative Analysis of Machine Learning Algorithms for Enhancing Stroke Prediction in Healthcare Database View
  4. Saidala R, Tetali A, Radder U, B P, M C, Reddy T. 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). Innovations in Stroke Evaluation: A Diagnostic System implementing Neuroimage-Based Machine Learning View
  5. Hamza M, Zaman M, Kehkashan T, Akbar F, Munir K, Shehzad A. 2024 5th International Conference on Innovative Computing (ICIC). Enhanced and Interpretable Brain Stroke Detection with High Accuracy using ViT B-16 Model View