Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis

Machine learning can accurately predict osteoporosis risk: The study found that XGBoost achieved 91% accuracy in identifying osteoporosis risk, suggesting ML models could help catch this "silent disease" before fractures occur, addressing a major public health challenge where the condition often goes undetected.

Explainability is essential for doctors to trust AI predictions: The research demonstrates that using explainability tools (SHAP, LIME, and Permutation Feature Importance) to show why a model makes predictions is crucial for healthcare applications, ensuring physicians can understand and confidently rely on the system's recommendations rather than treating it as a "black box."

AI-identified risk factors align with established medical knowledge: The study found that age, hormonal changes, and family history were the top predictors of osteoporosis risk—findings that match what doctors already know clinically. This validation suggests the AI system is learning medically meaningful patterns rather than spurious correlations.

Transparency builds accountability in healthcare AI: By revealing which factors drive individual risk predictions, explainable AI enables doctors to have informed conversations with patients about their specific osteoporosis risk factors and personalized prevention strategies, rather than simply accepting a prediction without understanding its basis.