An AI-assisted predictive health monitoring framework using wearable sensor data improved early health risk detection compared to conventional monitoring approaches.
Does an AI-assisted predictive health monitoring framework improve early health risk detection compared to conventional monitoring approaches?
An AI-assisted predictive health monitoring framework using wearable sensor data may improve early detection of health risks compared to traditional periodic examinations.
Healthcare monitoring systems are evolving rapidly with the integration of artificial intelligence, wearable sensors, and cloud-based data analytics. Traditional healthcare monitoring approaches rely on periodic medical examinations which may fail to detect early health risks. This research proposes an AI-assisted predictive health monitoring framework capable of analysing physiological data collected from wearable devices. The system processes health indicators such as heart rate, sleep patterns, and physical activity to identify abnormal trends and provide early alerts. Machine learning algorithms are employed to analyse patterns and support preventive healthcare monitoring. Experimental evaluation indicates that predictive analytics improves early health risk detection compared to conventional monitoring approaches. The proposed system highlights the importance of integrating intelligent analytics with digital healthcare systems.
Z et al. (2026) studied Health monitoring. AI-assisted predictive health monitoring framework vs. Conventional monitoring approaches was evaluated on Early health risk detection. An AI-assisted predictive health monitoring framework using wearable sensor data improved early health risk detection compared to conventional monitoring approaches.
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