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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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 34534 - 23
Hauptverfasser:	Corda, John Valerian, Karthikeyan, A., Zuber, Mohammad, Jacob, Meera, Ramos, Amith, Hosapatna, Mamatha, Dsouza, Anne, Pandey, Akhilesh Kumar, Ankolekar, Vrinda Hari
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 03.10.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 692/308 692/698 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
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Abstract	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.
AbstractList	Abstract 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. 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.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. 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.
ArticleNumber	34534
Author	Ramos, Amith Zuber, Mohammad Pandey, Akhilesh Kumar Corda, John Valerian Jacob, Meera Karthikeyan, A. Hosapatna, Mamatha Dsouza, Anne Ankolekar, Vrinda Hari
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Keywords	Random oversampling Mandible Machine learning algorithms Sexual dimorphism Synthetic minority oversampling technique Non-metric features
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Snippet	The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information regarding the... Abstract The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information...
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SubjectTerms	639/166 692/308 692/698 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
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Title	Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification algorithms
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