Open Access
Comparative Study of Machine Learning Models for Stroke Risk Prediction
4
Associate Professor, Department of Computer Science and Engineering, ITM (SLS) University, Vadodara, Gujarat, India
Abstract
A stroke is a chronic condition that occurs more and more every year and can be caused by the sudden stop of blood from reaching a part of the brain, causing serious neurological damage and a higher death rate if not caught on time. Early symptom detection offers insights that are important for stroke risk prediction and prompt clinical response for better stroke outcomes. This research proposes an effective machine learning-based framework for stroke risk prediction using the Kaggle Stroke Prediction Dataset. The approach involves thorough data preprocessing, such as filling missing values, label encoding, Min–Max normalization, and SMOTE–ENN data balancing, followed by the application of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers. The experimental results demonstrate exceptional predictive performance, where the proposed RF model achieved 99.99% accuracy, 99.97% precision, 99.98% recall, and 99.96% F1-score, while the proposed XGBoost model attained 99.92% accuracy, 99.91% precision, 99.90% recall, and 99.93% F1-score. These findings indicate that ensemble machine learning techniques significantly improve stroke risk prediction.
Keywords
Stroke risk prediction,
Machine learning,
Healthcare decision support
References
C. W. Tsao et al., “Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association,” Circulation, vol. 147, no. 8, Feb. 2023, doi: 10.1161/CIR.0000000000001123.
A. Warrier and K. Abhilash, “Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations,” in 2025 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), Bengaluru, India: IEEE, 2025, pp. 1450–1457, December. doi: 10.1109/ICICNIS66685.2025.11315712.
R. V. S. S. Bharattej R, H. M. Al-Jawahry, S. Rafikiran, D. Beula, and E. T. V. R. Kanth, “Blood Pressure Monitoring and Hypertension Prediction using Dynamic Opposite Learning Based Prairie Dog Optimization Algorithm,” in 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC), IEEE, Dec. 2024, pp. 1–7. doi: 10.1109/ICMNWC63764.2024.10872294.
M. Mittal, “The Transformative Role of CRM Systems in Modern Healthcare: Bridging the Provider-Patient Gap,” Int. J. Multidiscip. Res., vol. 6, no. 6, pp. 1–9, November-December, 2024.
G. Kayola et al., “Stroke Rehabilitation in Low- and Middle-Income Countries,” Am. J. Phys. Med. Rehabil., vol. 102, no. 2S, pp. S24–S32, Feb. 2023, doi: 10.1097/PHM.0000000000002128.
G. Y. H. Lip et al., “Integrated care for optimizing the management of stroke and associated heart disease: a position paper of the European Society of Cardiology Council on Stroke,” Eur. Heart J., vol. 43, no. 26, pp. 2442–2460, Jul. 2022, doi: 10.1093/eurheartj/ehac245.
V. S. E and R. D, “A Systematic Method of Stroke Prediction Model based on Big Data and Machine Learning,” in 2022 Smart Technologies, Communication and Robotics (STCR), IEEE, Dec. 2022, pp. 1–5. doi: 10.1109/STCR55312.2022.10009283.
S. Tiwari, A. Joshi, N. Rai, and P. Satpathy, “doi.org/10.1055/s-0041-1735323,” J. Neurosci. Rural Pract., vol. 12, p. 680, Sep. 2021, doi: 10.1055/s-0041-1735323.
V. L. Feigin et al., “Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization–Lancet Neurology Commission,” Lancet Neurol., vol. 22, no. 12, pp. 1160–1206, Dec. 2023, doi: 10.1016/S1474-4422(23)00277-6.
R. Kiruba, S. S. Rithish, P. Jagatheeswaran, R. Gokul, and S. Santhiya, “Prediction of Heart Stroke using Machine Learning Algorithms,” in 2024 5th International Conference on Smart Electronics and Communication (ICOSEC), IEEE, Sep. 2024, pp. 1798–1804. doi: 10.1109/ICOSEC61587.2024.10722063.
R. Lingam, “Agentic AI and Federated Learning for Privacy-Preserving Dental Imaging,” in 2026 Innovations in Machine, Engineering, and Digital Conference (IMED), Kota Kinabalu, Malaysia: IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/IMED68921.2026.11484887.
A. Gupta, Next-Generation Computing and Information Systems. London, India: CRC Press, 2026. doi: 10.1201/9781003648987.
R. V. S. S. Bharattej R, R. Riad Al-Fatlawy, S. Meenakshi Sundaram, E. A. Rathnakumari, and M. N. Sudha, “Quantum Variational Based Support Vector Machine for Early Detection of Sepsis,” in 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkuru, India: IEEE, Dec. 2024, pp. 1–5. doi: 10.1109/ICMNWC63764.2024.10872228.
S. A. D. Poovaiah, G. N. Mohan Sankaran, N. Jooluri, and M. Mukhlisa, “Alzheimer’s Disease Detection from MRI Scans using Deep Hybrid Architecture with Metaheuristic Hyperparameter Tuning,” in 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), Bhubaneswar, India: IEEE, 2025, pp. 1–7, July. doi: 10.1109/ISAC364032.2025.11156608.
N. Melnykova, R. Kulievych, Y. Vycluk, K. Melnykova, and V. Melnykov, “Anomalies Detecting in Medical Metrics Using Machine Learning Tools,” Procedia Comput. Sci., vol. 198, pp. 718–723, 2022, doi: 10.1016/j.procs.2021.12.312.
N.-B. Le, T.-T.-H. Pham, S.-H. Nguyen, N.-M. Nguyen, and T.-N. Nguyen, “AI-powered Predictive Model for Stroke and Diabetes Diagnostic,” Int. J. Intell. Syst. Appl., vol. 16, no. 1, pp. 24–40, Feb. 2024, doi: 10.5815/ijisa.2024.01.03.
T. Raj Ojha and A. Kumar Jha, “Analyzing the Performance of the Machine Learning Algorithms for Stroke Detection,” Int. J. Educ. Manag. Eng., vol. 13, no. 2, pp. 27–35, Apr. 2023, doi: 10.5815/ijeme.2023.02.04.
E. Dritsas and M. Trigka, “Stroke Risk Prediction with Machine Learning Techniques,” Sensors, vol. 22, no. 13, p. 4670, Jun. 2022, doi: 10.3390/s22134670.
T. Tazin, M. N. Alam, N. N. Dola, M. S. Bari, S. Bourouis, and M. Monirujjaman Khan, “Stroke Disease Detection and Prediction Using Robust Learning Approaches,” J. Healthc. Eng., vol. 2021, pp. 1–12, Nov. 2021, doi: 10.1155/2021/7633381.
A. Warrier, “Machine Learning-Enhanced Data Quality Validation for Electronic Health Record Integration,” Int. J. Sci. Technol., vol. 11, no. 1, pp. 1–8, Feb. 2020, doi: 10.71097/IJSAT.v11.i1.8387.
M. Mittal and S. Balamurugan, “Ethical Challenges in AI-Based Healthcare: Balancing Innovation With Patient Privacy And Fairness,” Int. J. Artif. Intell. Med., vol. 3, no. 2, pp. 1–20, Jul. 2025, doi: 10.34218/IJAIMED_03_02_001.
V. L. Feigin et al., “World Stroke Organization (WSO): Global Stroke Fact Sheet 2022,” Int. J. Stroke, vol. 17, no. 1, pp. 18–29, Jan. 2022, doi: 10.1177/17474930211065917.
M. O. Owolabi et al., “Primary stroke prevention worldwide: translating evidence into action,” Lancet Public Heal., vol. 7, no. 1, pp. e74–e85, Jan. 2022, doi: 10.1016/S2468-2667(21)00230-9.
J.-T. Kim et al., “Neural network-based clustering model of ischemic stroke patients with a maximally distinct distribution of 1-year vascular outcomes,” Sci. Rep., vol. 12, no. 1, p. 9420, Jun. 2022, doi: 10.1038/s41598-022-13636-w.
A. G, “Stroke Risk Prediction Using Imbalance-Sensitive Machine Learning Models on Structured Clinical Data,” in 2026 4th International Conference on Self-Sustainable Artificial Intelligence Systems (ICSSAS), IEEE, May 2026, pp. 715–720. doi: 10.1109/ICSSAS68835.2026.11559523.
S. Lila, Z. Altoka, S. Jukic, B. Karlik, and J. Kevric, “Evaluating Machine and Deep Learning Models for Stroke Prediction with Class Imbalance Handling,” in 2026 15th Mediterranean Conference on Embedded Computing (MECO), IEEE, Jun. 2026, pp. 1–4. doi: 10.1109/MECO70748.2026.11579030.
S. N. Pasha, R. Dadi, and K. Vennela, “Brain Stroke Prediction using Machine Learning and Neural Networks Models: A Comparative Study,” in 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), IEEE, Jun. 2025, pp. 1639–1646. doi: 10.1109/ICSSAS66150.2025.11080982.
M. Z. Hossain Zamil, M. R. Islam, S. Debnath, M. T. Mia, M. A. Rahman, and A. K. Biswas, “Stroke Prediction on Healthcare Data Using SMOTE and Explainable Machine Learning,” in 2025 13th International Symposium on Digital Forensics and Security (ISDFS), IEEE, Apr. 2025, pp. 1–6. doi: 10.1109/ISDFS65363.2025.11012059.
M. Sharma, I. Kaur, G. Saini, V. Thakur, and A. Mishra, “Predictive Modeling for Stroke Risk Assessment Using Machine Learning,” in 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), IEEE, May 2024, pp. 1–6. doi: 10.1109/ISCS61804.2024.10581155.
M. C. Das et al., “A comparative study of machine learning approaches for heart stroke prediction,” in 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), IEEE, Jul. 2023, pp. 1–6. doi: 10.1109/SmartNets58706.2023.10216049.
R. M. D. Saputra, Y. Chairul, D. Riana, A. S. Hewiz, and F. Aziz, “Stroke Prediction Based on Random Forest with SMOTE,” in 2023 International Conference on Information Technology Research and Innovation (ICITRI), IEEE, Aug. 2023, pp. 17–21. doi: 10.1109/ICITRI59340.2023.10249261.
Y. Li, Z. Wang, T. Wu, and T. Zhou, “Comparison of Six Machine Learning Algorithms for Stroke Risk Estimation,” Appl. Comput. Eng., vol. 8, no. 1, pp. 556–561, Aug. 2023, doi: 10.54254/2755-2721/8/20230274.
Z. He, “Machine Learning for Stroke Onset Risk Prediction and Model Comparison,” ITM Web Conf., vol. 88, p. 01037, Jul. 2026, doi: 10.1051/itmconf/20268801037.
A. S. M. A. S. Akib, R. Khawla, and A. Hasib, “Optimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI,” 2025.
Z. Gao, “Comparative Analysis of Machine Learning Models for Stroke Risk Prediction,” in Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy, SCITEPRESS - Science and Technology Publications, 2024, pp. 93–101. doi: 10.5220/0012998400004601.
Most read articles by the same author(s)
- Dr. Freddy Becket, RECENT DEVELOPMENTS IN THE TREATMENT AND MANAGEMENT OF LIVER CIRRHOSIS COMPLICATIONS , International Research Journal of Medical Sciences and Health Care: Vol. 2 No. 02 (2025): Volume 02 Issue 02
- Dr. James Miller, CIRCULATING MICRORNAS AS POTENTIAL BIOMARKERS IN THE DIAGNOSIS AND MANAGEMENT OF EPILEPSY , International Research Journal of Medical Sciences and Health Care: Vol. 2 No. 01 (2025): Volume 02 Issue 01
Similar Articles
- Dr. Hemant N. Patel, Artificial Intelligence Enabled Healthcare Analytics for Patient Risk Assessment and Prediction , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Maame Ama Owusuaa-Asante, Gordon Mabengban Yakpir, Kennedy Oberhiri Obohwemu, Jane Amedzro, Osinubi Olusunmola, Samuel Oluwatosin Adejuyitan, Oluwadamilola R. Tayo, Rupali Chauhan, Shubham Sharma, Divya Motupalli, Effectiveness of Aspirin for Stroke Prevention in High-Risk Vascular Patients: A Systematic Review and Meta-Analysis of Randomised Controlled Trials , International Research Journal of Medical Sciences and Health Care: Vol. 2 No. 05 (2025): Volume 02 Issue 05
- Dr. Huda Al-Shammari, Population-Level Assessment of Sex and Age Disparities in Acute Myeloid Leukemia Hospitalization Burden in U.S. Adults Using a Retrospective Healthcare Utilization Framework (2009–2018) , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Dr. Fatima Al-Nuaimi, Dr. Mariam Al-Mansoori, Integrating Hand Hygiene Compliance and Cardiopulmonary Risk Assessment in Hospitalized Patients with Liver Cirrhosis: A Theoretical Clinical Review of Safety, Prevention, and Prognostic Care , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Rizky Pratama, Dr. Putri Maharani, Risk Cascade Modeling of Nutritional Failure and Respiratory Infection in Infants with Labiopalatoschisis and Associated Dysphagia: A Case-Based Framework , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Rizky Pratama, Dr. Putri Maharani, Risk Cascade Modeling of Nutritional Failure and Respiratory Infection in Infants with Labiopalatoschisis and Associated Dysphagia: A Case-Based Framework , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Meera Nair, Evaluation of Antibiotic Strategies in Cardiovascular Patients Undergoing Dental Surgical and Restorative Care: A Clinical Study , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Oluremi Akintola-Idowu, Kennedy Oberhiri Obohwemu, Samrina Afzal, Rabeea Rizwan, Ibiangake Friday Ndioho, Joyce Eberechukwu Idomeh, Celestine Emeka Ekwuluo, Kevbe Ofobrukweta, Housing Circumstances and Mental Health Outcomes: A Systematic Review , International Research Journal of Medical Sciences and Health Care: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Sarah M. Dawson, Decoding Hand Actions Through Signal Analysis: Advancements in Prosthetic Limb Control , International Research Journal of Medical Sciences and Health Care: Vol. 2 No. 05 (2025): Volume 02 Issue 05
- Anzhela Voskanian, Filling the Inter-Eyelash Space as A Method of Optical Thickening of The Eyelash Row: Physiological and Aesthetic Reasons , International Research Journal of Medical Sciences and Health Care: Vol. 3 No. 01 (2026): Volume 03 Issue 01
1-10 of 27
Next
You may also start an advanced similarity search for this article.