Real-Time Health Analytics: Enhancing Wearable Technology for Proactive Healthcare Management

Abstract

The integration of wearable technology with real-time health analytics has the potential to transform healthcare by enabling continuous monitoring and proactive management of health parameters. This paper presents the development of advanced algorithms designed to process and analyze health data from wearables in real-time, aiming to detect anomalies and predict potential health issues before they become critical. The proposed method demonstrates a high level of accuracy, with performance metrics indicating an accuracy of 94.8%, a Root Mean Squared Error (RMSE) of 0.208, and a Mean Absolute Error (MAE) of 0.406. These results underscore the effectiveness of the proposed algorithms in identifying subtle patterns and deviations in health metrics, thereby facilitating timely interventions and improving patient outcomes. The findings highlight the promise of wearable health monitoring and real-time analytics in bridging the gap between data collection and actionable insights, ultimately fostering a more proactive and personalized approach to healthcare.