Design of Stroke Risk Warning System based on Internet of Things (IoT) Technology and Machine Learning Model

Stroke is one of the leading causes of death and disability worldwide, especially in the elderly and those with chronic underlying diseases. Early detection and timely intervention are key to minimizing the impact of stroke. In this context, this paper proposes the design of an early warning model for stroke risk based on Internet of Things (IoT) technology combined with machine learning models. The design model includes three main modules: (1) smart bracelet hardware device to collect biological data and fall protection device; (2) functions to analyze, evaluate, predict stroke risks and provide advice; (3) design functions for supervisors such as displaying biometric information, warnings, and smart advice via AI Chatbot to support first aid on mobile applications. Along with that, the paper proposes processing algorithms for the above modules. The system is expected to help improve monitoring efficiency and support early intervention in emergency situations related to stroke. To ensure the system operates effectively and accurately, the report used a dataset extracted from the reputable UCI website for training and testing the system.