用加权集成模型提升脑卒中诊断准确率
Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach
- 融合随机森林、深度学习等多模型预测结果
- 在私有数据集上达到94.91%准确率
- 适合医疗辅助诊断与早期风险筛查
脑卒中是由于大脑某部位血流中断导致细胞死亡的疾病。传统诊断方法如CT和MRI成本高且耗时。本研究提出一种加权投票集成（WVE）机器学习模型，结合随机森林、深度学习及基于直方图的梯度提升等分类器的预测结果，以更高效地预测脑卒中。该模型在私有数据集上实现了94.91%的准确率，有助于实现早期风险评估与预防。未来研究可探索优化技术进一步提升性能。
原文摘要 · Abstract (English)
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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