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Stroke is a medical condition in which the blood vessels in the brain rupture, causing brain damage. Symptoms may appear if the brain's flow of blood and other nutrients is disrupted. Stroke is the leading cause of death and disability worldwide, according to the World Health Organization (WHO). Early awareness of the numerous stroke warning symptoms can assist to lessen the severity of the stroke. To forecast the likelihood of a stroke happening in the brain, many machine learning (ML) models have been developed. This research uses a range of physiological parameters and machine learning algorithms, such as Support Vector Machine with extensive Exploratory Data Analysis, Random Forest Regression and PySpark. By using this methodologies and algorithms we got very high accuracy score results which are described down below.
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