Artificial neural networks in prediction of bone density among post-menopausal women

Abstract

Artificial neural networks (ANN) are promising tools in learning complex interplay of factors on a particular outcome. We performed this study to compare the predictive power of ANN and conventional methods in prediction of bone mineral density (BMD) in Iranian post-menopausal women. A database of 10 input variables from 2158 participants was randomly divided into training (1400), validation (150) and test (608) groups. Multivariate linear regression and ANN models were developed and validated on the training, and validation sets and outcomes (femoral neck and lumbar T-scores) were predicted and compared on the test group using different numbers of input variables. Results were evaluated by comparing the mean square of differences between predicted and reference values (non-central chi-square test) and by measuring area under the receiver operating characteristic curve (AUROC) around cut-off value of −2.5 for T-scores. For models with less than 3 input variables in femoral neck and 4 variables in spinal column, performance of regression and ANN models was almost the same. As more variables imported into models, ANN outperformed linear regression models. AUROC varied in 2 to 10 variable models as follows: for ANN in spine, from 0.709 to 0.774; linear models in spine, from 0.709 to 0.744; ANN in femoral neck, from 0.801 to 0.867; linear models in femoral neck, from 0.799 to 0.834. The ANN model performed better than five established patient selection tools in the test group. Superior performance of neural networks than linear models demonstrate their advantage especially in mass screening applications, when even a slight enhancement in performance results in significant decrease in number of misclassifications.