A Classification Based Approach to Predict the Gender Using Craniofacial Measurements

In archaeological, forensic, clinical and surgical fields, it is important to decide the gender of an individual and to know the facial asymmetry. The objective of this study is to ascertain the asymmetry of the skull and to predict the gender based on the shape and dimensions of the infraorbital foramina (IOF) and its position in relation to maxillary teeth, supraorbital foramen (SOF) and clinically relevant anatomical landmarks. Linear discriminant analysis (LDA), binary logistic regression (BLR), support vector machine (SVM) and bagging CART algorithms were used to predict the gender and results were validated using 10-fold cross-validation technique. A significant variation on the position of IOF in relation to anatomical landmarks was observed and the relative position of the IOF varies between the genders. Moreover, the left side measurements were larger than the right-side measurements and hence human skull is asymmetric. The outcome of the study proposes the classification-based approach to predict the gender of the individuals. The Bagging CART model performed well in terms of both accuracy and precision when compared with other methods.