A Comparative Analysis of Machine Learning Models for Stroke Prediction

Kripa Mary Jose; Nizar Banu; Melvin Infant A

doi:10.34256/ijcci2514

Kripa Mary Jose Department of Computer Science and Engineering, Christ Deemed to be University Bangalore, Karnataka, India
Nizar Banu Department of Computer Science and Engineering, Christ Deemed to be University Bangalore, Karnataka, India
Melvin Infant A Department of Computer Science and Engineering, Christ Deemed to be University Bangalore, Karnataka, India

Keywords: Stroke, Machine Learning, Risk prediction, Decision Tree, Random Forest, Neural Networks, Support Vector Machine, Elastic Net, Lasso, Neuroplasticity, Rehabilitation

Abstract

Stroke is a leading global health burden, and there is an urgent need for improvement in risk prediction and treatment. This paper examines the capability of several machine learning algorithms, including Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVMs), Elastic Nets, and Lasso, to predict stroke risk on four cardiovascular and stroke datasets. The results indicate that Decision Trees and Random Forests are always better than Neural Networks, although Neural Networks show promising accuracy. SVMs are consistent, while the Elastic Net and Lasso models give average results.

Metrics

Metrics Loading ...

References

W. J. Powers, A.A. Rabinstein, T. Ackerson, O.M. Adeoye, N.C. Bambakidis, K. Becker, José Biller, Michael Brown, Bart M. Demaerschalk, Brian Hoh, Edward C. Jauch, Chelsea S. Kidwell, Thabele M. Leslie-Mazwi, Bruce Ovbiagele, Phillip A. Scott, Kevin N. Sheth, Andrew M. Southerland, Deborah V. Summers, David L. Tirschwell, American Heart Association Stroke Council. (2019). Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 50(12), e344-e418. https://doi.org/10.1161/STR.0000000000000211

M. J. O'Donnell, S. L. Chin, S. Rangarajan, D. Xavier, L. Liu, H. Zhang,., P. Rao-Melacini, X. Zhang, P. Pais, S. Agapay, P. Lopez-Jaramillo, A. Damasceno, P. Langhorne, M.J. McQueen, A. Rosengren, M. Dehghan, Graeme J Hankey, A.L.Dans, A. Elsayed, A. Avezum, C. Mondo, H.C. Diener, D. Ryglewicz, A. Czlonkowska, N. Pogosova, C. Weimar, R. Iqbal, Rafael Diaz, K. Yusoff, A. Yusufali, A. Oguz, X. Wang, E. Penaherrera, F. Lanas, O.S. Ogah, A. Ogunniyi, H.K. Iversen, G. Malaga, Z. Rumboldt, S. Oveisgharan, F. Al Hussain, D. Magazi, Y. Nilanont, J. Ferguson, G. Pare, S. Yusuf, (2016). Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. The lancet, 388(10046), 761-775. https://doi.org/10.1016/S0140-6736(16)30506-2

N. Alageel, R. Alharbi, R. Alharbi, M. Alsayil, L.A. Alharbi, (2023). Using machine learning algorithm as a method for improving stroke prediction. International Journal of Advanced Computer Science and Applications, 14(4). 738- 744.

E. Dritsas, M. Trigka, (2022). Stroke risk prediction with machine learning techniques. Sensors, 22(13), 4670. https://doi.org/10.3390/s22134670

K. Mridha, S. Ghimire, J. Shin, A. Aran, M. M. Uddin and M. F. Mridha, Automated Stroke Prediction Using Machine Learning: An Explainable and Exploratory Study With a Web Application for Early Intervention, in IEEE Access, 11 (2023), 52288-52308. https://doi.org/10.1109/ACCESS.2023.3278273

S. Sahriar, S. Akther, J. Mauya, R. Amin, M.S. Mia, S. Ruhi, M.S. Reza, (2024). Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms. Heliyon, 10(5), e27411. https://doi.org/10.1016/j.heliyon.2024.e27411

T.S. Priyadarshini, M.A. Hameed, (2025). Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning. Measurement: Sensors, 37, 101405. https://doi.org/10.1016/j.measen.2024.101405

Gupta, A., Mishra, N., Jatana, N., Malik, S., Gepreel, K. A., Asmat, F., & Mohanty, S. N. (2025). Predicting stroke risk: an effective stroke prediction model based on neural networks. Journal of Neurorestoratology, 13(1), 100156. https://doi.org/10.1016/j.jnrt.2024.100156

E. Dritsas, M. Trigka, Stroke Risk Prediction with Machine Learning Techniques. Sensors, 22(13), (2022) 4670. https://doi.org/10.3390/s22134670

N. Biswas, K.M.M. Uddin, S.T. Rikta, S.K Dey, (2021) A Comparative Analysis of Machine Learning Classifiers for Stroke Prediction: A Predictive Analytics Approach. Healthcare Analytics, 2, 100116.

K. Akash Mahesh, H. N. Shashank, S. Srikanth S, A.M. Thejas, Prediction of Stroke Using Machine Learning. published via ResearchGate Institute of Technology (CMRIT), Bangalore

Y. Chahine, et al., ”Arrhythmia Risk and Stratification,” IEEE Reviews in Biomedical Engineering, vol. 15, pp. 211-225, 2022.

M. Chun, R. Clarke, B.J. Cairns, D. Clifton, D. Bennett, Y. Chen, Y. Guo, P. Pei, J. Lv, C. Yu, L. Yang, (2021) Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults. Journal of the American Medical Informatics Association, 28(8), 1719-1727. https://doi.org/10.1093/jamia/ocab068

Amann, J. (2022). Machine learning in stroke medicine: Opportunities and challenges for risk prediction and prevention. Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues, 57-71.

A. Jamthikar, D. Gupta, L. Saba, N.N. Khanna, T. Araki, K. Viskovic, S. Mavrogeni, J.R. Laird, G. Pareek, M. Miner, P.P. Sfikakis, A. Protogerou, V. Viswanathan, A. Sharma, A. Nicolaides, G.D. Kitas, J.S. Suri, (2020). Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovascular Diagnosis and Therapy, 10, 919–938. https://doi.org/10.21037/cdt.2020.01.07

S. Gangavarapu, G.L.A. Kumari, (2021) Analyzing the performance of stroke prediction using ML classification algorithms. International Journal of Advanced Computer Science and Applications, 12(6), 539–545. https://doi.org/10.14569/IJACSA.2021.0120662