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

Yao Zhiwan; Reza Zarrab; Jean Dubois

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arXiv · 2504.13974

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

Yao Zhiwan·Reza Zarrab·Jean Dubois

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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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Yao Zhiwan, Reza Zarrab, Jean Dubois. 2025-04-17. Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach. https://arxiv.org/abs/2504.13974

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