An Evaluation and Prediction of Osteoporosis Using Machine Learning Techniques

Author(s)	Dr. Uma G, Ms Sri Aishwarya A M
Country	India
Abstract	Osteoporosis is a silent, progressive bone disease that increases fracture risk, especially among aging and postmenopausal populations. This study focuses on the application of machine learning techniques to predict osteoporosis risk based on demographic, lifestyle, and medical variables. The dataset, sourced from Kaggle, includes 1,958 samples encompassing attributes such as age, gender, physical activity, calcium and vitamin D intake, hormonal status, prior fractures, and medication use. Multiple supervised machine learning algorithms Random Forest, Gradient Boosting, Decision Tree, AdaBoost, Logistic Regression, and Naïve Bayes were implemented using Python with a 70-30 train-test data split. Model performance was evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Among the tested models, [insert best-performing model] demonstrated the highest prediction accuracy. The results affirm the potential of ensemble learning methods in early osteoporosis detection, enabling proactive healthcare interventions and aiding clinical decision-making through automated risk assessment
Keywords	Osteoporosis, Machine Learning, Risk Prediction, Bone Mineral Density, Ensemble Models
Field	Mathematics > Statistics
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-28
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.45787

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