Enterprise AI Analysis
Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification algorithms
This study leverages machine learning (ML) to estimate sexual dimorphism in adult human mandibles from South India using non-metric morphological features. Analyzing 156 mandibles (102 male, 54 female) with four ML algorithms (KNN, Decision Tree, SVM, Random Forest), the study found Random Forest to be the most robust and accurate. It 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, demonstrating stable performance despite class imbalance. Key predictors identified were the N6 Gonial angle and N12 Flexure ramal post border. While ROS generally improved balanced accuracy and MCC for KNN, DT, and SVM, Random Forest's performance remained strong regardless of the oversampling technique. This research offers valuable insights for sex identification in forensic anthropology using non-metric mandibular features and ML.
Executive Impact: Key Findings at a Glance
Our analysis reveals significant advancements in automated sex determination:
0
Overall Accuracy
Achieved by Random Forest, demonstrating high reliability in sex determination.
0
F1 Score (RF)
Indicates excellent balance between precision and recall for Random Forest.
0
Jaccard Index (RF)
Reflects strong overlap between predicted and actual classifications for Random Forest.
0
Balanced Accuracy (ROS-RF)
Random Forest maintained high balanced accuracy regardless of oversampling.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Collect Mandible Data (Non-Metric)
→
Import to ML Analysis
→
Bifurcate Dataset (Train/Test)
→
Apply SMOTE/ROS for Balancing
→
Train ML Models (KNN, DT, SVM, RF)
→
Evaluate Performance (Accuracy, F1, Jaccard, MCC)
→
Identify Best Algorithm (Random Forest)
→
Feature Importance Analysis
→
Predict Sex
92%
Overall Accuracy with Random Forest
The Random Forest algorithm consistently achieved the highest overall accuracy of 92%, proving its superior capability in classifying sex based on non-metric mandibular features. This high accuracy was stable across both SMOTE and ROS oversampling methods.
| Algorithm | Jaccard Index | F1 Score | Accuracy | Balanced Accuracy | MCC |
|---|---|---|---|---|---|
| KNN | 0.78 | 0.87 | 0.87 | 0.88 | 0.78 |
| Decision Tree | 0.82 | 0.90 | 0.90 | 0.90 | 0.80 |
| SVM | 0.82 | 0.90 | 0.90 | 0.90 | 0.80 |
| Random Forest | 0.86 | 0.92 | 0.92 | 0.92 | 0.86 |
Conclusion: Random Forest consistently outperforms other algorithms across key metrics, especially after Random Over-Sampling (ROS), demonstrating its robust and reliable classification capability for sex determination. |
|||||
Forensic Anthropology & AI
Scenario: A forensic team faced a challenge in identifying individuals from fragmented remains where traditional metric measurements were unreliable. They needed a swift and accurate method for sex determination using available non-metric features.
Solution: Implementing an AI-driven classification system based on the non-metric features of mandibles, similar to the Random Forest model validated in this study. The system leveraged features like the N6 Gonial angle and N12 Flexure ramal post border, which were consistently identified as strong predictors.
Outcome: The AI system achieved 92% accuracy in sex determination, significantly speeding up the identification process and providing reliable results even with incomplete skeletal remains. The robustness of Random Forest ensured consistent performance, greatly enhancing the efficiency and success rate of forensic investigations.
Advanced ROI Calculator: Quantify Your Savings
Estimate the potential annual cost savings and reclaimed hours by integrating AI into your operations.
Annual Cost Savings
0
Annual Hours Reclaimed
0
AI Implementation Roadmap
Our structured approach ensures a smooth and effective integration of AI into your existing workflows.
Data Preparation & Pre-processing
Duration: 2 Weeks
Collection and digitization of non-metric mandibular features, including data cleaning and initial balancing using SMOTE/ROS.
Model Selection & Training
Duration: 3 Weeks
Evaluation of KNN, Decision Tree, SVM, and Random Forest. Training of the optimal Random Forest model with hyperparameter tuning.
Validation & Feature Importance
Duration: 2 Weeks
Cross-validation, performance metric calculation, and in-depth analysis of feature importance (e.g., N6 Gonial angle, N12 Flexure ramal post border).
Deployment & Integration
Duration: 3 Weeks
Integration of the validated AI model into forensic analysis workflows, including user interface development and system testing.
Ready to enhance your forensic identification capabilities with AI? Book a consultation to discuss implementing a robust machine learning solution for sex determination.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat