Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features

Isabela Bittencourt Basso, Pedro Felipe de Jesus Freitas, Aline Xavier Ferraz, Ana Julia Borkovski, Ana Laura Borkovski, Rosane Sampaio Santos, Rodrigo Nunes Rached, Erika Calvano Küchler, Angela Graciela Deliga Schroder, Cristiano Miranda de Araujo and Odilon Guariza-Filho

PLOS ONE, 2024, vol. 19, issue 11, 1-13

Abstract: Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machine learning algorithms. Cephalometric radiographs from 410 dental records of patients were screened to investigate the morphology of the coronoid process, condyle, and sigmoid notch and the Co-Gn distance. The following machine learning algorithms were used to build the predictive models: Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multilayer Perceptron Classifier, Random Forest Classifier, and Support Vector Machine (SVM). A 5-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and ROC curves were constructed. All tested variables demonstrated statistical significance (p

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312824 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 12824&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0312824

DOI: 10.1371/journal.pone.0312824

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().