Skip to content
DraconiticResearch
Search Sources API
181,713 studies31 sourcesUpdated 3 min ago
Peer-reviewedCC BY-NC-ND 4.0BMC Public Health

Predicting subsequent-year physical fitness assessment failure in university students using longitudinal data and explainable machine learning

  • Lan Luo
  • Fawei Huang
  • Baixia Li
Open at sourceDOICite

Related

  1. Predicting atropine treatment response heterogeneity in pediatric myopia using explainable machine learning: an on-treatment longitudinal monitoring framework
    Jun-Wei Chen, Chieh-Han Yu, Tzu-Chi Liu +2
    Scientific ReportsPeer-reviewedCC BYFull textEpidemiology & public healthDOAJ
  2. Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.
    İrem Akpolat, Fatma Çelik
    Diagnostics (Basel, Switzerland)Peer-reviewedCC BYFull textGeneral medicineEurope PMC
  3. Pain and fifty six subsequent wellbeing and life outcomes in a multicountry longitudinal study
    Lucía Macchia, R. Noah Padgett, Richard G. Cowden +2
    Communications MedicinePeer-reviewedCC BY 4.0General medicineNature
  4. Pre-diagnostic low muscle mass and sarcopenia associated with subsequent cancer in Chinese adults: a longitudinal CHARLS study
    Wensi Zhao, Xinyu Wu, Xiang Cheng +3
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textPhysiologyCrossref
  5. Explainable machine learning and deep learning models for predicting in-hospital mortality in burn patients
    Reza Zarei, Maral Taghizadeh Shalmaei, Masiha Mobayen +2
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textEpidemiology & public healthCrossref

Cite

BibTeXRIS
BibTeX
@article{luo2026predicting,
  title = {{Predicting subsequent-year physical fitness assessment failure in university students using longitudinal data and explainable machine learning}},
  author = {Lan Luo and Fawei Huang and Baixia Li},
  journal = {BMC Public Health},
  year = {2026},
  month = oct,
  doi = {10.1186/s12889-026-29742-7},
  url = {https://link.springer.com/10.1186/s12889-026-29742-7},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Predicting subsequent-year physical fitness assessment failure in university students using longitudinal data and explainable machine learning
AU  - Lan Luo
AU  - Fawei Huang
AU  - Baixia Li
PY  - 2026
DA  - 2026/10/05
JO  - BMC Public Health
DO  - 10.1186/s12889-026-29742-7
UR  - https://link.springer.com/10.1186/s12889-026-29742-7
SN  - 1471-2458
ER  -