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From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring

Author

Listed:
  • Katarzyna Staszak

    (Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland)

  • Bartosz Tylkowski

    (Eurecat, Centre Tecnològic de Catalunya, C/Marcellí Domingo s/n, 43007 Tarragona, Spain)

  • Maciej Staszak

    (Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland)

Abstract

The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people’s needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.

Suggested Citation

  • Katarzyna Staszak & Bartosz Tylkowski & Maciej Staszak, 2023. "From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring," IJERPH, MDPI, vol. 20(5), pages 1-20, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4605-:d:1088287
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    References listed on IDEAS

    as
    1. Valueva, M.V. & Nagornov, N.N. & Lyakhov, P.A. & Valuev, G.V. & Chervyakov, N.I., 2020. "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 232-243.
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