AI-Powered Cardiovascular Risk Prediction: Integrating Wearable Biosensors and Machine Learning Algorithms

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Integration of wearable biosensors and AI analytics improves cardiovascular risk prediction accuracy, highlighting lifestyle factors' role.

Improved cardiovascular risk prediction accuracy integrates wearable biosensors with AI-driven analytics.
Analysis from 385 responses revealed HRV monitoring and AI analytics enhanced prediction accuracy significantly.
Exploratory factor analysis confirmed validity within socio-technical and biopsychosocial frameworks.
Integration into digital health ecosystems can personalize risk assessments, although results stem from cross-sectional data.

Quan et al. (2025) studied this question.

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Integration of wearable biosensors and AI analytics improves cardiovascular risk prediction accuracy, highlighting lifestyle factors' role.