Prediction of gender by odontometric data using logistic regression analysis

Determination of gender by anthropologists can be done by various methods e.g. skull bones, pelvic bones and other skeletal determinants. Assessment of odontometric data is a promising tool for gender prediction, which is usually preserved due to its robust nature.

The objective of the study was to predict the gender of an individual, using the odontometric data with powerful statistical tools like Logistic Regression Analysis (LRA) and Discriminant Analysis (DA).

100 subjects were selected (50 male and 50 female) within the age group of 18–28 years. An alginate impression was made and models were prepared. The odontometric data was collected in the form of various mesiodistal and buccolingual measurements with Vernier calipers which was subjected to statistical analysis, using the two tests LRA and DA. Thereafter, results were compared for accurate gender prediction.

The statistical analysis of the measurements obtained was done by using two tests logistic regression analysis and discriminant analysis. After analysis and comparisons of the two methods for gender prediction, it was observed that LRA provides more accurate prediction than DA in determining the gender. Also, when data from both the arches was analyzed, it was more accurate in predicting the gender as in comparison to the analysis from either of the arch.

The study has revealed that LRA may be better than DA for odontometric sex prediction. Overall, the results depict that the complete dentition, when used as a unit and through the application of flexible multivariate statistics such as LRA, has potential for its use as a prominent and sole indicator of sex prediction.