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Jeena, R.S. and Kumar, S. (2016) Stroke Prediction Using SVM. 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 16-17 December 2016, 600-602.
https://doi.org/10.1109/iccicct.2016.7988020

has been cited by the following article:

• TITLE: The Early Warning Signs of a Stroke: An Approach Using Machine Learning Predictions

  AUTHORS: Esraa H. Augi, Almabruk Sultan

  KEYWORDS: Machine Learning, Stroke, k-Nearest Neighbors, Decision Tree, Random Forest, GXboost

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.6, June 25, 2024

  ABSTRACT: Early stroke prediction is vital to prevent damage. A stroke happens when the blood flow to the brain is disrupted by a clot or bleeding, resulting in brain death or injury. However, early diagnosis and treatment reduce long-term needs and lower health costs. We aim for this research to be a machine-learning method for forecasting early warning signs of stroke. The methodology we employed feature selection techniques and multiple algorithms. Utilizing the XGboost Algorithm, the research findings indicate that their proposed model achieved an accuracy rate of 96.45%. This research shows that machine learning can effectively predict early warning signs of stroke, which can help reduce long-term treatment and rehabilitation needs and lower health costs.