Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study.
- DOI
- 10.3233/shti260220
- Published
- 2026 May 21
- Container
- Studies in health technology and informatics
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3233/shti260220,
title = {Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study.},
author = {Chaitokkia S and Toontom N and Boonnuk T and Yusamran N and Nghiep LK and Tudpor K},
year = {2026},
journal = {Studies in health technology and informatics},
doi = {10.3233/shti260220},
url = {https://doi.org/10.3233/shti260220}
}RIS
TY - JOUR TI - Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study. AU - Chaitokkia S AU - Toontom N AU - Boonnuk T AU - Yusamran N AU - Nghiep LK AU - Tudpor K PY - 2026 JO - Studies in health technology and informatics DO - 10.3233/shti260220 UR - https://doi.org/10.3233/shti260220 ER -
APA
S, C., N, T., T, B., N, Y., LK, N., & K, T. (2026). Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study.. Studies in health technology and informatics. https://doi.org/10.3233/shti260220
Source records
- pubmed · retrieved 2026-09-26T17:29:35.317Z