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This research develops an IoT-enabled predictive model for early stroke detection and continuous patient monitoring, combining XGBoost with IoT devices to collect and analyze health data in real time. The model integrates machine learning techniques, including feature selection and regularization, achieving high accuracy in stroke prediction. Evaluated using a Kaggle dataset, it demonstrates improved sensitivity for early detection based on risk factors like age, blood pressure, and lifestyle. These findings highlight the potential of IoT and machine learning to transform stroke care by enabling proactive intervention and reducing healthcare system burdens.

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IoT-Enhanced Predictive Models for Brain Stroke Care Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Purna Chandra Rao Kandimalla,
  • T. Anuradha

摘要

This research develops an IoT-enabled predictive model for early stroke detection and continuous patient monitoring, combining XGBoost with IoT devices to collect and analyze health data in real time. The model integrates machine learning techniques, including feature selection and regularization, achieving high accuracy in stroke prediction. Evaluated using a Kaggle dataset, it demonstrates improved sensitivity for early detection based on risk factors like age, blood pressure, and lifestyle. These findings highlight the potential of IoT and machine learning to transform stroke care by enabling proactive intervention and reducing healthcare system burdens.