Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique

Abstract

This study presents a predictive modeling framework for osteoporosis risk assessment using ensemble techniques, specifically XGBoost and Bagging. Leveraging a dataset comprising comprehensive health factors influencing osteoporosis development, including demographic details, lifestyle choices, medical history, and bone health indicators, the aim is to facilitate accurate identification of individuals at risk. The dataset consists of 1958 samples, evenly distributed between osteoporosis-positive and osteoporosis-negative cases. The methodology involves the separation of features and labels, followed by data splitting into training and testing sets. XGBoost, a powerful gradient boosting algorithm, is employed as the base estimator within a Bagging ensemble, enhancing predictive accuracy and generalization. The model is trained on the training set and evaluated using cross-validation techniques to ensure robustness and mitigate overfitting. The results of the classification report demonstrate promising performance metrics, with an overall accuracy of 88% on the test set. Precision and recall scores indicate strong predictive capabilities, particularly in correctly identifying osteoporosis-positive cases. The novel integration of XGBoost within a Bagging ensemble provides an innovative approach to osteoporosis risk prediction, harnessing the strengths of both algorithms to improve model performance. This research contributes to the advancement of osteoporosis management and prevention strategies by providing a reliable tool for early risk assessment. The combination of machine learning techniques with comprehensive health data offers a valuable approach to personalized healthcare, enabling targeted interventions and optimized resource allocation. Ultimately, this study aims to enhance patient outcomes and reduce the burden of osteoporosis-related morbidity and mortality.