Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification algorithms

The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information regarding the male and female distinctions. Machine learning algorithms are widely used for various applications due to their accuracy and reliability, exte...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 34534 - 23
Main Authors: Corda, John Valerian, Karthikeyan, A., Zuber, Mohammad, Jacob, Meera, Ramos, Amith, Hosapatna, Mamatha, Dsouza, Anne, Pandey, Akhilesh Kumar, Ankolekar, Vrinda Hari
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 03.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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692/698
Accuracy
Adult
Algorithms
Anthropology
Artificial intelligence
Bones
Cardiovascular disease
Classification
Classification Algorithms
Consent
Data collection
Datasets
Decision Trees
Engineering
Feature selection
Female
Females
Forensic Anthropology - methods
Humanities and Social Sciences
Humans
India
Learning algorithms
Machine Learning
Machine learning algorithms
Male
Males
Mandible
Mandible - anatomy & histology
Medicine
Morphology
multidisciplinary
Neural networks
Non-metric features
Random oversampling
Science
Science (multidisciplinary)
Sex Characteristics
Sex determination
Sex Determination by Skeleton - methods
Sexual dimorphism
Statistical analysis
Support Vector Machine
Support vector machines
Synthetic minority oversampling technique
Young Adult
India
Random oversampling
Mandible
Machine learning algorithms
Sexual dimorphism
Synthetic minority oversampling technique
Non-metric features
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information regarding the male and female distinctions. Machine learning algorithms are widely used for various applications due to their accuracy and reliability, extending their application in biological profiling. This study aims to estimate sexual dimorphism using various machine-learning algorithms based on non-metric features of the mandible. This study uses four machine-learning algorithms—k-nearest neighbors, decision tree, support vector machines, and random forest to determine sex based on 12 mandibular non-metric parameters. The data was collected from three medical institutes in Karnataka, India, involving a sample of 156 individuals. Random Forest consistently achieved the highest Jaccard Index (0.86), F1 score (0.92), and accuracy (0.92) across both SMOTE and Random Over-Sampling (ROS) methods, showing stable and robust performance. ROS improved balanced accuracy for KNN, Decision Tree, and SVM by up to 9.7%. Feature importance analysis highlighted N6 Gonial angle and N12 Flexure ramal post border as key predictors. Statistical tests found no significant accuracy differences among models. Female specificity remained lower across all models. This study offers insights into employing machine learning algorithms for sex identification using non-metric observations of the mandible.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-17831-3