Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques


  •   Senjuti Rahman

  •   Mehedi Hasan

  •   Ajay Krishno Sarkar


The brain is the human body's primary upper organ. Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. The World Health Organization (WHO) claims that stroke is the leading cause of death and disability worldwide. Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. Several classification models, including Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, Random Forest, Decision Tree, Logistic Regression, K Neighbors, SVM - Linear Kernel, Naive Bayes, and deep neural networks (3-layer and 4-layer ANN) were successfully used in this study for classification tasks. The Random Forest classifier has 99% classification accuracy, which was the highest (among the machine learning classifiers). The three layer deep neural network (4-Layer ANN) has produced a higher accuracy of 92.39% than the three-layer ANN method utilizing the selected features as input. The research's findings showed that machine learning techniques outperformed deep neural networks.

Keywords: Deep Neural Network, Extreme Gradient Boosting, Machine Learning, Stroke Prediction.


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How to Cite
Rahman, S., Hasan, M. and Sarkar, A.K. 2023. Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques. European Journal of Electrical Engineering and Computer Science. 7, 1 (Jan. 2023), 23–30. DOI: