用11个特征和集成模型提升中风预测准确率。
Ensemble of Convolutional Neural Networks for StrokePrediction: Towards Improved Diagnostic Accuracy
- 结合多种机器学习算法，采用集成策略提高预测稳定性。
- 随机森林等集成方法准确率达99.52%，优于单模型。
- 适合医疗AI研究者与临床辅助诊断系统开发者参考。
脑卒中具有高死亡率和高发病率，对健康构成重大威胁，需快速干预以提高生存率。早期诊断与预防可显著降低死亡与残疾风险。近年来深度学习推动了计算机辅助诊断技术的发展。本研究提出一种智能系统，基于11个特征，通过7种监督学习算法进行中风预测。流程包括文献综述、数据可视化、预处理及模型评估。集成方法如随机森林、堆叠分类器和袋装分类器均达到99.52%的高准确率，决策树为98.24%。其他模型如KNN和TabNet分别取得96.73%和96.49%的准确率。自定义前馈神经网络达94.91%，而SVC和逻辑回归分别为88.06%和77.03%。结果表明，集成方法在中风分类中表现优异。
原文摘要 · Abstract (English)
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.
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