Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach
By: Yao Zhiwan, Reza Zarrab, Jean Dubois
Potential Business Impact:
Helps doctors find strokes faster and prevent them.
A brain stroke occurs when blood flow to a part of the brain is disrupted, leading to cell death. Traditional stroke diagnosis methods, such as CT scans and MRIs, are costly and time-consuming. This study proposes a weighted voting ensemble (WVE) machine learning model that combines predictions from classifiers like random forest, Deep Learning, and histogram-based gradient boosting to predict strokes more effectively. The model achieved 94.91% accuracy on a private dataset, enabling early risk assessment and prevention. Future research could explore optimization techniques to further enhance accuracy.
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