Brain Stroke Detection Using Machine Learning

Authors

  • G. Ravi Kumar Assistant Professor, Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Puttur, India
  • P. Vyshnavi Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Puttur, India
  • S. Prasanna Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Puttur, India
  • T. Harshavardhan Reddy Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Puttur, India
  • C. Charanya Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Puttur, India
  • P. Chandrababu Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Puttur, India

Keywords:

Case sheets, Support Vector Machine, XG Boost, SGD, Decision Tree, Random Forest

Abstract

This gives a general algorithm to classify the stroke using different machine learning algorithms with the help of stroke data set. Machine Learning algorithms can be used in different sectors such as surveillance, health, Auto mobiles etc. In the proposed idea we have to collect the data from the different cases from the patients and arrange this data in data set. By using the dataset, we have to train the machine by busing different machine learning algorithms such as, support vector machine, XG Boost, SGD, Decision tree and random forests. In these algorithms, Random Forest achieves high accuracy.

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Published

12-03-2022

How to Cite

[1]
G. R. Kumar, P. Vyshnavi, S. Prasanna, T. H. Reddy, C. Charanya, and P. Chandrababu, “Brain Stroke Detection Using Machine Learning”, IJRESM, vol. 5, no. 3, pp. 34–36, Mar. 2022.

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Articles