An Improved Machine Learning Approach for Predicting Ischemic Stroke

An Improved Machine Learning Approach for Predicting Ischemic Stroke

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-1
Year of Publication : 2021
Authors : N. Suresh Kumar, M. Thangamani, V. Sasikumar, S. Nallusamy
DOI :  10.14445/22315381/IJETT-V69I1P217

Citation 

MLA Style: N. Suresh Kumar, M. Thangamani, V. Sasikumar, S. Nallusamy. "An Improved Machine Learning Approach for Predicting Ischemic Stroke" International Journal of Engineering Trends and Technology 69.1(2021):111-115. 

APA Style:N. Suresh Kumar, M. Thangamani, V. Sasikumar, S. Nallusamy. An Improved Machine Learning Approach for Predicting Ischemic Stroke  International Journal of Engineering Trends and Technology, 69(1), 111-115.

Abstract
Stroke occurs when appearing the blockage of tissues of the brain of blood coagulation. This obstruction can show up at the collar or in the brain. Clot, as a rule, begins in the heart and moves by the circulatory context. Clotting can be distinct all unaided or get stopped in a strip. When it obstructs a mind corridor, the cerebrum doesn`t get enough blood or oxygen, and cells begin to die. Stroke is the major cause of blockage of blood in tissues where the brain`s oxygen and blood supply. Enters of disease control and prevention decide stroke is one of the main reasons for death. In the US, 7 95,000 peoples have a stroke in the year 2020. The major symptoms are trouble walking, trouble speaking, loss of balance, blurred vision, etc. This research mainly focuses on finding the stroke and the major types of strokes. Stroke is a major role in the peoples who have suffered more. This research uses Recursive Feature Elimination using the Cross-Validation (RFECV) algorithm to eliminate similar strokes.

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Keywords
Ischemic Stroke, Machine Learning, IST Dataset, Elimination, Classification Modules, Prediction