Prediction of hip fracture in post-menopausal women using artificial neural network approach.

Abstract

Hip fracture is one of the most serious health problems among post-menopausal women with osteoporosis. It is very difficult to predict hip fracture, because it is affected by multiple risk factors. Existing statistical models for predicting hip fracture risk yield area under the receiver operating characteristic curve (AUC) ~0.7-0.85. In this study, we trained an artificial neural network (ANN) to predict hip fracture in one cohort, and validated its predictive performance in another cohort. The data for training and validation included age, bone mineral density (BMD), clinical factors, and lifestyle factors which had been obtained from a longitudinal study that involved 1167 women aged 60 years and above. The women had been followed up for up to 10 years, and during the period, the incidence of new hip fractures was ascertained. We applied feed-forward neural networks to learn from the data, and then used the learning for predicting hip fracture. Results of prediction showed that the accuracy of model I (which included only lumbar spine and femoral neck BMD) and model II (which included non-BMD factors) was 82% and 84%, respectively. When both BMD and non-BMD factors were combined (Model III), the accuracy increased to 87%. The AUC for model III was 0.94. These findings indicate that ANNs are able to predict hip fracture more accurately than any existing statistical models, and that ANNs can help stratify individuals for clinical management.