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Brain Stroke Prediction Using Learning Machines & Deep Learning

EasyChair Preprint no. 10707

14 pagesDate: August 15, 2023


A stroke can happen if blood flow suddenly to a region of the brain stops. Depending on damaged part of the brain, disability is caused by a lack of blood flow because progressively losing brain cells perish. Predicting strokes & promoting healthy living can both benefit substantially from early detection of symptoms. In this research, there are several models developed & evaluat-ed using machine learning (ML) in sequence to provide a strong pattern for the stroke incidence risk prediction over the long run. The main contribution of this study is a stacking technique that performs well and is supported by numerous measures, consisting of. K nearest neighbor, logistic regression, XG boost, random forest classifier, decision tree classifier, adaboost, cat-boost, etc.

Keyphrases: data analysis, machine learning, risk prediction, Stroke.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Samman Ashraf and Zunaira Akram and Umair Muneer Butt and Asia Sharif},
  title = {Brain Stroke Prediction Using Learning Machines & Deep Learning},
  howpublished = {EasyChair Preprint no. 10707},

  year = {EasyChair, 2023}}
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