用于卒中预测的卷积神经网络集成：迈向更高的诊断准确率
Ensemble of Convolutional Neural Networks for StrokePrediction: Towards Improved Diagnostic Accuracy
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中文总结 AI 辅助
本研究提出基于11项特征的智能卒中预测系统，对比7种监督机器学习算法，发现Random Forest等集成方法准确率达99.52%，凸显其在卒中分类中的有效性。
中文摘要 AI 辅助
脑卒中具有高死亡率和高发病率，会带来重大健康风险，需要快速干预以提高生存率。早期诊断和预防措施可大幅减少生命损失与残疾。深度学习的最新进展催生了用于早期卒中检测的新型计算机辅助诊断技术。本研究提出一种智能系统，利用11项特征预测潜在卒中，通过7种监督机器学习算法进行评估。流程包括文献综述、数据集可视化、数据预处理和模型评估。Random Forest（随机森林）、Stacking Classifier（堆叠分类器）、Bagging Classifier（装袋分类器）等集成方法达到99.52%的高准确率，Decision Tree（决策树）准确率为98.24%；KNN和TabNet等其他模型表现可靠，准确率分别为96.73%和96.49%；自定义前馈模型准确率为94.91%，SVC和逻辑回归准确率较低，分别为88.06%和77.03%。结果凸显了集成方法在卒中分类中的有效性。
英文摘要
Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early diagnosis and preventive measures can greatly reduce life loss and disabilities. Recent advancements in deep learning have led to novel computer-aided diagnostic techniques for early stroke detection. This study proposes an intelligent system that predicts potential strokes using eleven features, evaluated through seven supervised machine learning algorithms. The process includes a literature review, dataset visualization, data preprocessing, and model evaluation. Ensemble methods like Random Forest, Stacking Classifier, and Bagging Classifier achieved high accuracies of 99.52%, while Decision Tree reached 98.24%. Other models, including KNN and TabNet, demonstrated reliable performance, achieving accuracies of 96.73% and 96.49%, respectively. The custom feedforward model achieved 94.91%, while SVC and logistic regression had lower accuracies at 88.06% and 77.03%. The results highlight the effectiveness of ensemble methods in stroke classification.
