Osteoporosis Prediction Using Hybrid Logistic Regression: A Simple yet Effective Classification Approach

Abstract

Osteoporosis is a prevalent condition that leads to an increased risk of fractures due to reduced bone density, particularly in aging populations. Early detection and risk assessment are critical for effective management and prevention. This study proposes a novel hybrid model utilizing Logistic Regression combined with feature selection and optimization techniques to enhance the prediction accuracy of osteoporosis risk. The hybrid approach leverages the simplicity and interpretability of Logistic Regression while incorporating advanced techniques to address data imbalances and improve model robustness. We evaluated the model on a clinical dataset, comparing its performance against traditional models. The results demonstrate that the hybrid Logistic Regression model provides a more reliable prediction of osteoporosis, with increased precision and recall, making it an effective tool for early diagnosis in clinical practice. This model holds promise for integrating into medical decision-making systems, aiding in targeted treatment strategies for at-risk individuals.