Predictive Model for Brain Stroke Detection
DOI:
https://doi.org/10.48001/978-81-966500-2-5-1Keywords:
Brain Stroke Prediction, Machine Learning, Random Forest, Medical Data Analysis, Healthcare AnalyticsAbstract
Strokes significantly impact the central nervous system and rank among the leading causes of death globally. The most damaging types are ischemic and hemorrhagic strokes, with the World Health Organization (WHO) reporting that 3% of people suffer from subarachnoid hemorrhage, 10% from intracerebral hemorrhage, and 87% from ischemic stroke. Strokes result from disrupted blood flow to the brain, often due to arterial blockages or damage. This project aims to develop a Python-based machine learning model for accurate stroke prediction, using classification algorithms such as Random Forest and Bagging Classifiers. These models offer promising tools for assisting medical professionals in diagnosing strokes, enabling earlier intervention and personalized care, potentially reducing the long-term effects and improving patient outcomes.
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References
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