这项研究提出了一个利用深度学习算法预测中风的新方法，准确率高达99.52%，值得医疗领域关注。与传统的SVC和逻辑回归相比，集成方法显著提升了诊断准确性。
本研究提出一种智能系统，利用11个特征预测潜在中风，通过7种监督机器学习算法评估。集成方法如随机森林、堆叠分类器和Bagging分类器实现了99.52%的高准确率，决策树达到98.24%。KNN和TabNet分别实现了96.73%和96.49%的准确率。自定义前馈模型达到94.91%，而SVC和逻辑回归的准确率较低，分别为88.06%和77.03%。结果表明，集成方法在中风分类中非常有效。
Ensemble of Convolutional Neural Networks for StrokePrediction: Towards Improved Diagnostic Accuracy
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.