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

2025-05-01

AI Summary

Early osteoporosis screening in asymptomatic populations remains challenging due to low clinical interpretability and limited predictive reliability. Method: We systematically developed and compared six machine learning classifiers—including Random Forest, XGBoost, and LightGBM—optimized via GridSearchCV. Crucially, we innovatively integrated three explainable AI (XAI) methods—SHAP, LIME, and permutation feature importance—to jointly validate model decisions against clinical domain knowledge. Contribution/Results: XGBoost achieved superior performance (accuracy: 91%, precision: 0.92, recall: 0.91, F1-score: 0.90). SHAP analysis identified age as the most influential predictor, while multi-method XAI consistently highlighted age, hormonal changes, and family history as key risk drivers. This integrative framework delivers a high-fidelity, traceable, and clinically interpretable risk assessment tool, advancing trustworthy AI deployment for early osteoporosis detection.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable artificial intelligence (XAI) to improve model transparency. Osteoporosis is a significant public health concern, sometimes remaining untreated owing to its asymptomatic characteristics, and early identification is essential to avert fractures. The research assesses six machine learning classifiers: Random Forest, Logistic Regression, XGBoost, AdaBoost, LightGBM, and Gradient Boosting and utilizes a dataset based on clinical, demographic, and lifestyle variables. The models are refined using GridSearchCV to calibrate hyperparameters, with the objective of enhancing predictive efficacy. XGBoost had the greatest accuracy (91%) among the evaluated models, surpassing others in precision (0.92), recall (0.91), and F1-score (0.90). The research further integrates XAI approaches, such as SHAP, LIME, and Permutation Feature Importance, to elucidate the decision-making process of the optimal model. The study indicates that age is the primary determinant in forecasting osteoporosis risk, followed by hormonal alterations and familial history. These results corroborate clinical knowledge and affirm the models' therapeutic significance. The research underscores the significance of explainability in machine learning models for healthcare applications, guaranteeing that physicians can rely on the system's predictions. The report ultimately proposes directions for further research, such as validation across varied populations and the integration of supplementary biomarkers for enhanced predictive accuracy.

Problem

• Predicting osteoporosis risk using machine learning models
• Improving model transparency with explainable AI techniques
• Identifying key clinical factors influencing osteoporosis risk

Innovation

• Uses six ML classifiers for osteoporosis risk prediction
• Applies XAI techniques like SHAP, LIME for transparency
• Identifies age as key risk factor via explainable analysis