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Stroke is a life-threatening medical condition caused by an inadequate blood supply to the brain. According to the World Health Organization (WHO), stroke is a leading cause of death and disability worldwide. After a stroke, the affected brain areas fail to function normally, making early detection of warning signs crucial for effective treatment and reducing disease severity. Various Machine Learning (ML) and Deep Learning (DL) models have been developed to predict stroke occurrence. This research highlights the effectiveness of Federated Learning (FL), a decentralized training approach that bolsters privacy while preserving model performance. Our models outperform traditional ML and DL methods, achieving an accuracy of 98%. Evaluations using metrics such as accuracy, precision, recall, and F1 score confirm the robustness and generalizability of our approach.

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Federated Deep Learning Models for Stroke Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Asma Mansour,
  • Olfa Besbes,
  • Takoua Abdellatif

摘要

Stroke is a life-threatening medical condition caused by an inadequate blood supply to the brain. According to the World Health Organization (WHO), stroke is a leading cause of death and disability worldwide. After a stroke, the affected brain areas fail to function normally, making early detection of warning signs crucial for effective treatment and reducing disease severity. Various Machine Learning (ML) and Deep Learning (DL) models have been developed to predict stroke occurrence. This research highlights the effectiveness of Federated Learning (FL), a decentralized training approach that bolsters privacy while preserving model performance. Our models outperform traditional ML and DL methods, achieving an accuracy of 98%. Evaluations using metrics such as accuracy, precision, recall, and F1 score confirm the robustness and generalizability of our approach.