Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. Through rigorous experimentation, we validate the effectiveness of our ensemble model using the AUC metric. Through comparing our findings with those of other models in the field, we gain valuable insights into the merits and drawbacks of various approaches. This, in turn, contributes significantly to the progress of machine learning and deep learning techniques specifically in the domain of stroke prediction.

翻译：中风预测在预防和管理这一致残性疾病中发挥着关键作用。本研究利用综合性数据集应对中风预测的挑战，提出了一种融合XGBoost与xDeepFM算法的集成模型。我们的工作旨在通过提高预测准确性与鲁棒性，改进现有中风预测模型。通过严格的实验验证，我们采用AUC指标证实了该集成模型的有效性。通过与领域内其他模型的对比分析，我们深入洞察了不同方法的优劣之处。这进而为机器学习与深度学习技术在中风预测领域的进步做出了重要贡献。