An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing
  XGBoost and xDeepFM Algorithms
Weinan Dai Yifeng Jiang Chengjie Mou Chongyu Zhang
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.