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

Yao Zhiwan, Reza Zarrab, Jean Dubois
4/17/2025
cs.LGcs.AI

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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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Cite this paper

@article{zhiwan2025enhancing,
  title  = {Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach},
  author = {Yao Zhiwan and Reza Zarrab and Jean Dubois},
  year   = {2025},
  eprint = {2504.13974},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.13974v1}
}

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