International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.13, No.2, Apr. 2023

Analyzing the Performance of the Machine Learning Algorithms for Stroke Detection

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Trailokya Raj Ojha, Ashish Kumar Jha

Index Terms

Brain stroke, machine learning, data analysis, prediction


A brain stroke is a condition with an insufficient blood supply to the brain, which causes cell death. Due to the lack of blood supply, the brain cells die, and disabilities occurs in different parts of the brain. Strokes have become one of the major causes of death and disability in recent years. Investigating the affected individuals has shown several risk factors that are considered to be causes of stroke. Considering such risk factors, many research works have been performed to classify and predict stroke. In this research, we have applied five machine learning algorithms to identify and classify the stroke from the individual’s medical history and physical activities. Different physiological factors have are considered and applied to machine learning algorithms such as Naïve Bayes, AdaBoost, Decision Table, k-NN, and Random Forest. The algorithm Decision Table performed the best to predict the stroke based on different physiological factors in the applied dataset with an accuracy of 82.1%. The machine learning algorithms can be a helpful for clinical prediction of stroke against individual’s medical history and physical activities in a better way. 

Cite This Paper

Trailokya Raj Ojha, Ashish Kumar Jha, "Analyzing the Performance of the Machine Learning Algorithms for Stroke Detection ", International Journal of Education and Management Engineering (IJEME), Vol.13, No.2, pp. 27-35, 2023. DOI:10.5815/ijeme.2023.02.04


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