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Stability-Driven Osteoporosis Screening: Multi-View Consensus Feature Selection with External Validation and

Waragunt Waratamrongpatai1, Watcharaporn Cholamjiak2, Nontawat Eiamniran2

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Osteoporosis risk assessment can be simplified, as age and medication use are key predictors. Minimal models focusing on these factors show comparable performance to complex ones, aiding efficient screening.

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Area of Science:

  • Osteoporosis research
  • Predictive modeling in healthcare
  • Epidemiology of bone diseases

Background:

  • Osteoporosis poses a significant global health challenge, necessitating effective early risk assessment for fracture prevention.
  • Current screening tools often use demographic, clinical, and lifestyle factors, but their predictive importance varies across datasets.

Purpose of the Study:

  • To evaluate the stability and behavior of established osteoporosis risk factors using statistical and machine learning methods.
  • To determine if simplified models can achieve predictive performance comparable to comprehensive models.
  • To assess the generalizability of predictive models across different data sources.

Main Methods:

  • Analysis of two datasets: an open-access Kaggle dataset (n=1958) and a hospital-based retrospective dataset (n=176).
  • Feature relevance assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring.
  • Model performance evaluated using decision trees, SVM, k-NN, Naïve Bayes, and efficient linear classifiers with varying feature sets (full to minimal).

Main Results:

  • Age consistently emerged as the strongest predictor, followed by corticosteroid use; other factors had limited additional predictive value.
  • Simplified models (age-based or age + medication) achieved high accuracy (≈91%) and AUC (≈0.95), comparable to full models.
  • Near-minimal models including gender showed a good balance of discrimination and efficiency, though performance decreased with distributional shifts.

Conclusions:

  • Stability-driven feature selection confirms known epidemiological risk patterns rather than discovering novel predictors.
  • Minimal and near-minimal models offer methodological efficiency and acceptable performance, especially when including gender.
  • Results are preliminary; further multi-center studies are needed to confirm generalizability and clinical utility for screening.