An explainable predictive machine learning model of osteopenia for perimenopausal women based on clinical data: a retrospective single-center study - Summary - MDSpire

An explainable predictive machine learning model of osteopenia for perimenopausal women based on clinical data: a retrospective single-center study

  • By

  • Xiaoling Zhuo

  • Huixian Zeng

  • Huoqiang Chen

  • Yunlin Wang

  • Zhenhua Feng

  • Quanhui Liang

  • May 29, 2026

Share

Objective:

To develop a machine learning-based model for identifying individuals at risk of osteopenia in perimenopausal women using clinical data.

Key Findings:
  • 17 predictors of osteopenia were identified.
  • Random forest demonstrated the best performance with an AUC of 0.978 in training and 0.933 in validation datasets.
  • Key predictors included menopause, age, PINP, β-CTX, height, and eGFR.
  • A web-based calculator was developed for clinical use.
  • RF outperformed other models with an AUPR of 0.93 for the top six features.
Interpretation:

Limitations:
  • The study is based on a single center, which may limit generalizability.
  • The retrospective design may introduce bias.
Conclusion:

Original Source(s)

An explainable predictive machine learning model of osteopenia for perimenopausal women based on clinical data: a retrospective single-center study

  • by Xiaoling Zhuo, Huixian Zeng, Huoqiang Chen, Yunlin Wang, Zhenhua Feng, Quanhui Liang

  • May 29, 2026

Related Content

Multidisease Detection from Fundus Imaging

Researchers in China create AI framework aimed at using retinal imaging to screen for multiple systemic diseases

  • June 8, 2026

  • 3 min
An explainable machine learning model for predicting osteoporotic fragility fractures: a retrospective study in South China

Body composition’s effect on the bone–vascular axis of osteoporosis discovered in AI-based CT analysis of COPD patients