Enhancing Sex Estimation Accuracy with Cranial Angle Measurements and Machine Learning.

ABSTRACT: The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms. Computed tomography images of 154 males and 180 females were used to derive data of 36 cranial angles. The classification models were created by support vector machines, naïve Bayes, logistic regression, and the rule-induction algorithm CN2. A series of cranial angle subsets was arranged by an attribut

SUBMITTER: Toneva D 

PROVIDER: S-EPMC11504716 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature