Machine learning applied to wearable fitness tracker data and the risk of hospitalizations and cardiovascular events

Background: Wearable fitness trackers generate extensive physiological and activity data, offering potential to monitor health and predict outcomes. Machine learning (ML) techniques applied to these data may enable early identification of adverse health conditions, such as hospitalizations and devel...

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Bibliographic Details
Main Authors: John Kundrick, Aditi Naniwadekar, Virginia Singla, Krishna Kancharla, Aditya Bhonsale, Andrew Voigt, Alaa Shalaby, N.A. Mark Estes, Sandeep K Jain, Samir Saba
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:American Journal of Preventive Cardiology
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Online Access:http://www.sciencedirect.com/science/article/pii/S2666667725000819
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