Distributed and Collaborative Learning Approach for Stroke Prediction
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55729-3_13
Reference17 articles.
1. Senthilkumar, S.A., Rai, B.K., Meshram, A.A., Gunasekaran, A., Chandrakumarmangalam, S.: Big data in healthcare management: a review of literature. Am. J. Theor. Appl. Bus. 4, 57–69 (2018)
2. Feigin, V.L., et al.: World Stroke Organization (WSO): global stroke fact sheet. Int. J. Stroke 17(1), 18–29 (2022). https://doi.org/10.1177/17474930211065917
3. Singh, M.S., Choudhary, P.: Stroke prediction using artificial intelligence. In: Proceedings of the 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), Bangkok, Thailand, pp. 158–161 (2017). https://doi.org/10.1109/IEMECON.2017.8079581
4. Jeena, R.S., Kumar, S.: Stroke prediction using SVM. In: Proceedings of International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 600–602 (2016). https://doi.org/10.1109/ICCICCT.2016.7988020
5. Alanazi, E.M., Abdou, A., Luo, J.: Predicting Risk of stroke from lab tests using machine learning algorithms: development and evaluation of prediction models. JMIR Form Res. 5(12), e23440 (2021). https://doi.org/10.2196/23440
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