Brain Stroke Prediction System – A Review

Authors

  • Ramandeep Kaur Student, Department of Computer Science Engineering, DAV Institute of Engineering and Technology, Jalandhar, India
  • Sirjana Dhillon Student, Department of Computer Science Engineering, DAV Institute of Engineering and Technology, Jalandhar, India

Keywords:

Machine Learning, Brain stroke, Logistic Regression, Decision Tree classification, Random Forest classification, KNN, SVM

Abstract

The primary organ of the human body that regulates all bodily activities is the brain. When the blood supply to a portion of the brain is cut off, brain tissue cannot receive oxygen and nutrients, which results in a stroke. In minutes, brain cells start to degenerate. fundamental organ of the body, in charge of all bodily processes. When the blood supply to a portion of the brain is cut off, brain tissue cannot receive oxygen and nutrients, which results in a stroke. In minutes, brain cells start to degenerate. The World Health Organization (WHO) reports that stroke is the second greatest cause of death in the world, accounting for about 11% of all fatalities. Subarachnoid haemorrhage affects 3% of the population, intracerebral haemorrhage affects 10%, and ischemic stroke affects 87% of the population. Brain stroke symptoms can appear suddenly and may include facial drooping, weakness, or paralysis. Those who have heart disease, high blood pressure, or other risk factors are also more likely to experience brain stroke. Based on input characteristics including gender, age, various diseases, and smoking status, our ML model uses a dataset to predict whether a patient is likely to have a stroke. A trustworthy dataset for stroke prediction was collected from the Kaggle website to increase the algorithm's efficacy. For precise prediction, we have employed Machine Learning techniques including Logistic Regression, Decision Tree Classification, Random Forest Classification, KNN, and SVM.

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Published

18-06-2023

Issue

Section

Articles

How to Cite

[1]
R. Kaur and S. Dhillon, “Brain Stroke Prediction System – A Review”, IJMDES, vol. 2, no. 6, pp. 21–24, Jun. 2023, Accessed: Jul. 21, 2024. [Online]. Available: https://journal.ijmdes.com/ijmdes/article/view/156