A comparison between neural network and other metric methods to determine sex from the upper femur in a modern French population

One such request led us to compare the predictive accuracy of different mathematical methods using four non-standard measurements of the proximal femur (trochanter-diaphysis distance (TD), greater-lesser trochanter distance (TT), greater trochanter width (TW) and trochanter-head distance (TH)). These measurements were taken on 76 femurs (38 males and 38 females) of French individuals. Intra- and inter-observer trials did not reveal any significant statistical differences. The predictive accuracy of three models built using linear and non-linear modelling techniques was compared: discriminant analysis, logistic regression and neural network. The neural network outperformed discriminant analysis and, to a lesser extent, logistic regression. Indeed, the best results were obtained with a neural network that correctly classified 93.4% of femurs, with similar results in males (92.1%) and females (94.7%). Univariate functions were less accurate (68-88%). Discriminant analysis and logistic regression, both using all four variables, led to slightly better results (88.2% and 89.5%, respectively). In addition, all the models, save the neural network, led to unbalanced results between males and females. In conclusion, the artificial neural network is a powerful classification technique that may improve the accuracy rate of sex determination models for skeletal remains.