Article: Performance evaluation of machine learning classifiers for brain stroke prediction Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2024 Vol.20 No.1 pp.61 - 77 Abstract: A cerebral vascular accident, commonly known as a stroke, is a pathological condition that impacts the brain due to the rupture of capillaries. It occurs when there is a disturbance in the typical blood circulation and essential physiological processes of the brain. As per the WHO, stroke is the foremost aetiology of mortality, a significant public health concern. While there has been considerable research on the prognosis of heart attacks, investigating the risk factors associated with strokes has been relatively limited. Considering this, a plethora of advanced machine learning models has been leveraged to prognosticate the probability of an impending stroke event. The prime focus of this study is the performance evaluation of eight distinct machine learning classification models as support vector classifier, K-nearest neighbour, logistic regressor, decision tree classifier, random forest classifier, Naïve Bayes classifier, AdaBoost classifier, and XGBoost classifier used for brain stroke prediction. The performance statistics obtained through experimental setup shows that the XGBoost algorithm demonstrated remarkable accuracy, yielding prediction results of approximately 92.75%, making it the preeminent model for precise and reliable stroke prediction. Inderscience Publishers - linking academia, business and industry through research

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Title: Performance evaluation of machine learning classifiers for brain stroke prediction

Authors: Drishti Arora; Rakesh Garg; Farhan Asif; Ritvik Garg; Neetu Singla

Addresses: Department of Computer Science and Engineering, Amity University Uttar Pradesh, Noida, India ' Department of Computer Science and Engineering, Amity University Uttar Pradesh, Noida, India ' Department of Computer Science and Engineering, Amity University Uttar Pradesh, Noida, India ' Department of Computer and Communication Engineering, Manipal University, Jaipur, Rajasthan, India ' Department of Computer Science and Engineering, The NorthCap University, Gurugram, India

Abstract: A cerebral vascular accident, commonly known as a stroke, is a pathological condition that impacts the brain due to the rupture of capillaries. It occurs when there is a disturbance in the typical blood circulation and essential physiological processes of the brain. As per the WHO, stroke is the foremost aetiology of mortality, a significant public health concern. While there has been considerable research on the prognosis of heart attacks, investigating the risk factors associated with strokes has been relatively limited. Considering this, a plethora of advanced machine learning models has been leveraged to prognosticate the probability of an impending stroke event. The prime focus of this study is the performance evaluation of eight distinct machine learning classification models as support vector classifier, K-nearest neighbour, logistic regressor, decision tree classifier, random forest classifier, Naïve Bayes classifier, AdaBoost classifier, and XGBoost classifier used for brain stroke prediction. The performance statistics obtained through experimental setup shows that the XGBoost algorithm demonstrated remarkable accuracy, yielding prediction results of approximately 92.75%, making it the preeminent model for precise and reliable stroke prediction.

Keywords: brain stroke prediction; machine learning classifiers; accuracy; AUC score.

DOI: 10.1504/IJBRA.2024.137369

International Journal of Bioinformatics Research and Applications, 2024 Vol.20 No.1, pp.61 - 77

Received: 06 Jun 2023
Accepted: 24 Jul 2023
Published online: 14 Mar 2024 *

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Title: Performance evaluation of machine learning classifiers for brain stroke prediction

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