Predictive Modeling for Osteoporosis Risk Assessment from DXA Scans

A common skeletal condition marked by decreased bone density, osteoporosis is a major global health concern, especially for elderly populations. This study aims to provide a novel method for evaluating osteoporosis risk by combining relevant clinical data with information from Dual-energy X-ray Absorptiometry (DXA) scans. Using a deductive technique and an interpretive attitude, we compiled a large dataset of clinical data and DXA scans from various patient groups. To ensure data integrity, sophisticated data preparation procedures were used, such as alignment, standardization, and management of missing data. To extract pertinent data, characteristic engineering and selection procedures were used, producing a cleaned dataset for modeling. The comprehensive predictive model was created using machine learning techniques such as support vector machines, random forests, and logistic regression. Thorough analysis produced encouraging findings, with R-squared values showing that the model fit the data very well. Furthermore, interpretability methods like Partial Dependence Plots (PDPs) and Shapley Additive explanations (SHAP) values offered insightful information about the significance and connections between features.