Smart AI-Powered Machine Learning Risk Assessment for Early Osteoporosis Detection for Women Bone Health

Osteoporosis is often called a silent disease because it progresses without symptoms until a fracture occurs, posing a serious, yet frequently overlooked, threat to women health. In response to the pressing need for early detection, we introduce OsteoInsight, an intelligent, AI-powered web application designed to assess osteoporosis risk with both clinical accuracy and interpretability. Built on a Random Forest classifier trained on over 2000 women health records, our model incorporates a wide range of domain-informed features, including hormonal history, lifestyle factors, reproductive health, and conditions affecting bone health. Despite an imbalanced dataset, with around 75% of cases being osteoporosis-positive, the model achieved encouraging results: 71.81% accuracy, an F1-score of 0.79, and an AUC-ROC of 0.78. SHAP analysis highlighted age, BMI, and menstrual history as key predictors, offering transparent insights into the model reasoning. Additional contributors like fracture history, signs of low estrogen, and lactation duration were also found to be significant, enriching the interpretability of predictions. These insights are seamlessly integrated into OsteoInsight user interface, making risk assessments not only accessible but also understandable for both clinicians and users. Our findings underscore the potential of AI-driven tools to enhance early screening and enable personalized risk profiling, empowering women and healthcare providers to take proactive steps in osteoporosis prevention.