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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Machine learning-driven clinical decision support for low bone mineral density: A web-based prediction model with

Xing Yang1, Jianyuan Liu2, Xiaozhi Huang3

  • 1Health Management Research Institute, People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, Nanning, China.

Bone
|July 16, 2025
PubMed
Summary

This study developed an interpretable machine learning model to predict low bone mineral density (LBMD), offering a free and accessible web tool for improved LBMD risk assessment and cost-effective screening.

Keywords:
LIMELogistic regressionLow bone mineral densityMachine learningPrediction modelRisk factorsSHAP

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Bone Health Research

Background:

  • Low bone mineral density (LBMD), encompassing osteopenia and osteoporosis, presents significant healthcare costs.
  • Current diagnostic methods for LBMD lack optimal accuracy and accessibility.
  • There is a need for advanced, interpretable tools for LBMD risk assessment.

Purpose of the Study:

  • To develop an interpretable machine learning model for predicting LBMD risk.
  • To implement the model as a user-friendly, web-based clinical decision support tool.
  • To enhance the accessibility and cost-effectiveness of LBMD screening.

Main Methods:

  • Utilized dual-energy X-ray absorptiometry (DXA) data from a large cohort and an external NHANES dataset.
  • Employed LASSO and logistic regression for feature selection, identifying key LBMD predictors.
  • Evaluated ten machine learning models, with logistic regression (LR) showing superior performance, validated by AUC, MCC, Brier score, and DCA.
  • Interpreted model decisions using SHAP and LIME, and deployed the optimal LR model as a Streamlit web application.

Main Results:

  • Identified age, BMI, alkaline phosphatase, and total cholesterol as key predictors of LBMD.
  • The logistic regression model achieved high predictive performance: AUC of 0.902 (internal) and 0.812 (external).
  • Decision curve analysis confirmed the clinical utility of the LR model.
  • SHAP and LIME analyses provided consistent explanations for the model's predictions.

Conclusions:

  • An interpretable machine learning model was successfully developed for LBMD prediction.
  • The web-based tool offers a free, reliable, and accessible solution for LBMD risk assessment.
  • This advancement significantly improves the cost-effectiveness and accessibility of LBMD screening.