A Machine Learning Approach for Determination of Gender of Sri Lankan People by Using the Dental Arch Dimensions

Dental arch dimensions play a crucial role in orthodontic and prosthodontic treatments, defined by dental arch morphology and widely utilised across various fields. This study focuses on classifying gender based on dental arch dimensions among Sri Lankan individuals. The dataset includes arch dimensions of 573 individuals from different provinces, excluding the Eastern Province. Statistical tests, such as Student's t-test, Kruskal-Walli's test, and Analysis of Variance (ANOVA), were applied to assess the significance of arch dimensions concerning Gender and Ethnicity. Most dimensions showed significance. Various visualizations were employed, and classification methods, including K-nearest neighbours (KNN), Support Vector Machines (SVM), Naïve Bayes (NB) Classifier, Decision tree, and Random Forest (RF) Classifier, were used for gender prediction. Results revealed high accuracy, with the up-sampling SVM model achieving the highest accuracy. Sensitivity, specificity, and F1 scores were notable for up-sampling SVM models and the rose function RF classifier model, achieving the highest F1 score. In conclusion, up-sampling SVM and rose function RF classifier models effectively classify gender based on available dental arch dimensions.