Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach

• URL: http://arxiv.org/abs/2504.13974v1
• Date: Thu, 17 Apr 2025 21:54:36 GMT
• Title: Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach
• Authors: Yao Zhiwan, Reza Zarrab, Jean Dubois,
• Abstract summary: A brain stroke occurs when blood flow to a part of the brain is disrupted, leading to cell death.<n>Traditional stroke diagnosis methods, such as CT scans and MRIs, are costly and time-consuming.<n>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.<n>The model achieved 94.91% accuracy on a private dataset, enabling early risk assessment and prevention.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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