Multiclass Osteoporosis Detection Using Woodpecker-Optimized CNN-XGBoost & predicting Diagnostic Accuracy via A Machine Learning Approach

Osteoporosis, a disease that weakens bones and increases fracture risk, requires early detection for effective management. This study presents a novel machine learning model combining CNN and XGBoost, optimized with the Woodpecker algorithm, for multiclass osteoporosis detection. The model achieved high accuracy across multiple datasets, including X-ray images, BMD, and clinical data, outperforming traditional methods. The full feature set showed superior performance, especially in multimodal datasets, with reduced false positives and false negatives. The proposed approach offers a promising tool for improving osteoporosis diagnosis, with potential for future application to larger datasets and clinical settings. The model was evaluated across several datasets, including X-ray images, bone mineral density (BMD), DXA scans, fracture risk assessments, and clinical data, using multiple metrics such as accuracy, precision, recall, and F1-score. The full feature set outperformed the reduced feature set, achieving an overall accuracy of over 90% in the training, validation, and testing phases. The model's robustness was particularly evident in multimodal datasets, where integrating imaging and clinical data resulted in significantly reduced false positives and false negatives.
The study concludes that the Woodpecker-optimized CNN-XGBoost model offers a promising tool for enhancing the early detection of osteoporosis. Future research may focus on expanding the model's applicability to larger datasets and incorporating explainability techniques to increase its interpretability for clinical use. This approach has the potential to significantly improve osteoporosis classification and diagnosis, providing a foundation for more accurate, efficient, and scalable AI-driven solutions in healthcare