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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.13974 |
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| _version_ | 1866913799643070464 |
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| author | Zhiwan, Yao Zarrab, Reza Dubois, Jean |
| author_facet | Zhiwan, Yao Zarrab, Reza Dubois, Jean |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach Zhiwan, Yao Zarrab, Reza Dubois, Jean Machine Learning Artificial Intelligence 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. |
| title | Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13974 |