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Systematic Assessment of Mammalian Skull Specimens for Dental and Temporomandibular Joint Pathology

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Published on: August 22, 2022

Machine learning-based classification of population affinity in two North Indian populations using morphological

Damini Siwan¹, Ankita Guleria², Rakesh Meena³

¹Department of Forensic Science, Panjab University, Sector-14, Chandigarh, India.

Legal Medicine (Tokyo, Japan)

|July 6, 2026

Summary

This study used machine learning models to classify population groups based on dental traits for forensic identification. Logistic Regression achieved the highest accuracy, demonstrating its potential in forensic odontology.

Keywords:

Dental morphology Forensic identification Forensic odontology Machine learning North Indian population groups

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Area of Science:

Forensic Science
Anthropology
Computer Science

Background:

Forensic odontology utilizes dental evidence for personal identification.
Morphological Dental Traits (MDTs) aid in classifying population groups for forensic identification.
Machine Learning (ML) offers advanced analytical capabilities for forensic applications.

Purpose of the Study:

To classify population affinity between two North Indian groups (Khasas and Kolis) using MDTs.
To evaluate and compare the performance of various ML models for this classification task.
To identify the optimal ML model for population affinity estimation based on dental features.

Main Methods:

Collected dental casts from 434 participants (18-40 years) from two distinct North Indian populations.
Observed and recorded Morphological Dental Traits (MDTs).
Employed Recursive Feature Elimination (RFE) for feature selection and trained ML models including SVM, LR, DT, RF, and GBM.

Main Results:

Logistic Regression (LR) achieved the highest classification accuracy at 75.8%, followed by Support Vector Machine (SVM).
Decision Tree (DT) and Gradient Boosting (GBM) models showed the lowest accuracy at 64.3%.
ROC-AUC and F1 score analyses corroborated LR as the top-performing model.

Conclusions:

Machine learning approaches, particularly Logistic Regression, are effective for population affinity estimation using dental features in forensic odontology.
This method can significantly support disaster victim identification and crime scene investigations involving dental remains.
The study highlights the utility of ML in enhancing the precision of forensic identification processes.