Comparison of logistic and Bayesian classifiers for evaluating the risk of femoral neck fracture in osteoporotic patients

Femoral neck fracture prediction is an important social and economic issue. The research compares two statistical methods for the classification of patients at risk for femoral neck fracture: multiple logistic regression and Bayes linear classifier. The two approaches are evaluated for their ability to separate femoral neck fractured patients from osteoporotic controls. In total, 272 Italian women are studied. Densitometric and geometric measurements are obtained from the proximal femur by dual energy X-ray absorptiometry. The performances of the two methods are evaluated by accuracy in the classification and receiver operating characteristic curves. The Bayes classifier achieves an accuracy approximately 1% higher than that of the multiple logistic regression. However, the performances of the two methods, evaluated by the area under the curves, are not statistically different. The study demonstrates that the Bayes linear classifier can be a valid alternative to multiple logistic regression in the classification of osteoporotic patients