Brain Stroke Prediction Using Learning Machines & Deep Learning

EasyChair Preprint 10707

Abstract

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

@booklet{EasyChair:10707,
  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 10707},
  year      = {EasyChair, 2023}}