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An Ensemble Machine Learning Technique for Stroke Prognosis

Mesfer Al Duhayyim1,*, Sidra Abbas2,*, Abdullah Al Hejaili3, Natalia Kryvinska4, Ahmad Almadhor5, Uzma Ghulam Mohammad6

1 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University Al-Kharj, 16273, Saudi Arabia
2 Department of Computer Science, COMSATS University, Islamabad, 53000, Pakistan
3 Computer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
4 Information Systems Department, Faculty of Management, Comenius University in Bratislava, Odbojárov, Bratislava, 440, Slovakia
5 Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia
6 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan

* Corresponding Authors: Mesfer Al Duhayyim. Email: email; Sidra Abbas. Email: email

Computer Systems Science and Engineering 2023, 47(1), 413-429.


Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting model based on three Machine Learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). We apply data preprocessing to manage the outliers and useless instances in the dataset. Furthermore, to address the problem of imbalanced data, we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique (SMOTE), allowing it to engage in the learning process actively. Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96% accuracy, 0.97% precision, 0.97% recall, and 0.96% F1-score. The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.


Cite This Article

M. A. Duhayyim, S. Abbas, A. A. Hejaili, N. Kryvinska, A. Almadhor et al., "An ensemble machine learning technique for stroke prognosis," Computer Systems Science and Engineering, vol. 47, no.1, pp. 413–429, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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