Research on Machine Learning-Based Intelligent Wearable Early Warning System for Heart Rhythm Abnormalities

With the development of intelligent wearable devices, machine learning-based early warning systems for heart rhythm abnormalities have shown significant potential in the field of health monitoring. This study designed an intelligent wearable early warning system using machine learning to collect, analyze, and predict heart rhythm abnormalities in real time. First, the hardware design and data transmission modules of the wearable device were introduced. By combining features of heart rhythm abnormalities, multiple machine learning algorithms (e.g., Support Vector Machine, Convolutional Neural Network) were used for data modeling and analysis. Extensive experiments were conducted to validate the system’s accuracy and practical applicability in detecting heart rhythm abnormalities. The results showed that the system achieved high detection accuracy and effectively reduced the false-positive and false-negative rates of abnormal heart rhythm events. Furthermore, this study explored security and privacy protection measures to ensure the safety of users’ personal data. The system holds significant application value and development potential in the future of intelligent health monitoring.