Machine Learning Models for Predicting Brain Strokes

Ahmed Mostafa Ibrahim, Samaa Ahmed Mostafa Ibrahim; Elzanfaly, Doaa; Yakoub, Ahmed

doi:10.21608/fcihib.2022.76906.1048

Machine Learning Models for Predicting Brain Strokes

Document Type : Original Article

Authors

¹ Information System Department , faculty of computers and artificial intelligence , Helwan University

² Information system department , faculty of computers and artificial intelligence , helwan university

³ Information System Department , Faculty of computers and artificial intelligence , helwan university

10.21608/fcihib.2022.76906.1048

Abstract

Strokes are one of the most serious and common diseases in the world due to their sudden and rapid occurrence. It is the second leading cause of death and the third cause of disability worldwide.It's the death of brain cells as a result of not provide these cells with the appropriate amount of blood, It leads to death in a few minutes. So predicting strokes on the basis of
Risk factors for the patient is one of the most important reasons to prevent these strokes and provide early treatment for them. Machine learning techniques are used to build predictive stroke models according to the patient's electrolyte health record, which contains factors leading to stroke.
Ensemble methods are one of the most important concepts in machine learning. They are working to collect more machine learning algorithms and combine them and produce one reliable predictive model with higher accuracy
The purpose of this paper is to provide a survey of predictive models.
Strokes using a machine learning squad work.

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