Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Jiandong Huang, Jinling Wang, Elaine Ramsey, Gerard Leavey, Timothy Chico, Joan Condell

Research output: Contribution to journalReview articlepeer-review

33 Citations (Scopus)
114 Downloads (Pure)


Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardio-vascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
Keywords: cardiovascular disease; wearable sensor devices; artificial intelligence (AI); machine learning (ML); deep learning (DL)
Original languageEnglish
Article number8002
Pages (from-to)1- 28
Number of pages28
Issue number20
Publication statusPublished (in print/issue) - 20 Oct 2022

Bibliographical note

Funding Information:
This work is funded by the eCareWell project. UK Community Renewal Fund supported by HM Treasury.

Publisher Copyright:
© 2022 by the authors.


  • cardiovascular disease
  • wearable sensor devices
  • artificial intelligence (AI);
  • machine learning (ML);
  • deep learning (DL)
  • artificial intelligence (AI)
  • machine learning (ML)


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