Brain Stroke Prediction Using Machine Learning & Python Flask
Keywords:
Stroke Prediction, Cerebrovascular accident (CVA), Machine Learning Model, Brain Blood SupplyAbstract
A stroke (cerebrovascular accident, CVA) happens when part of the brain doesn’t get enough blood, causing loss of function in the affected area. It can be ischemic (due to blocked blood flow) or hemorrhagic (caused by bleeding in the brain). Stroke is a medical emergency that can lead to death or permanent disability, and ischemic strokes need immediate treatment within hours.
A mini-stroke (transient ischemic attack, TIA) causes temporary symptoms that go away within 24 hours but still requires urgent medical care to prevent future strokes. According to the WHO, stroke is the third leading cause of death worldwide, responsible for 10.7% of total deaths. Our machine learning (ML) model predicts stroke risk based on key factors such as age, gender, glucose level, blood pressure, married or unmarried and smoking status, focusing on major stroke risk factors.
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References
8.1 Research Papers & Journals
• WHO Stroke Report (2022) – World Health Organization, Available at: www.who.int
• M. Gupta & A. Sharma (2021) – "Predicting Stroke Risk Factors Using AI-Based Models" (IEEE Access).
• K. Das, et al. (2020) – "Stroke Prediction Using Machine Learning Algorithms" (International Journal of Healthcare Informatics).
• Manisha Sirsat, Eduardo Ferme, Joana Camara, “Machine Learning for Brain Stroke: A Review,”.
• Harish Kamal, Victor Lopez, Sunil A. Sheth, “Machine Learning in Acute Ischemic Stroke Neuroimaging,”.
8.2 Websites
• Flask Documentation - Lightweight
• IEEE Xplore Digital Library
• Kaggle Datasets - Open datasets for stroke prediction.
• Scikit-learn Documentation - Machine learning tools and techniques used in the project.
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