Summary of An Explainable Machine Learning Approach For Age and Gender Estimation in Living Individuals Using Dental Biometrics, by Mohsin Ali et al.
An Explainable Machine Learning Approach for Age and Gender Estimation in Living Individuals Using Dental Biometrics
by Mohsin Ali, Haider Raza, John Q Gan, Ariel Pokhojaev, Matanel Katz, Esra Kosan, Dian Agustin Wahjuningrum, Omnina Saleh, Rachel Sarig, Akhilanada Chaurasia
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research develops a predictive system for age and gender estimation in living individuals using dental measurements such as Coronal Height (CH), Coronal Pulp Cavity Height (CPCH), and Tooth Coronal Index (TCI). Machine learning models including Cat Boost Classifier, Gradient Boosting Machine, Ada Boost Classifier, Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Extra Trees Classifier are employed to analyze dental data from 862 living individuals. A novel ensemble learning technique is developed, combining multiple models tailored to distinct dental metrics for accurate age and gender estimation. The study also creates an explainable AI model using SHAP, enabling dental experts to make informed decisions based on comprehensible insights. Results show that Random Forest and eXtreme Gradient Boosting models yield the highest F1 scores for age and gender estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how we estimate someone’s age and gender using their teeth. Scientists used a special type of data from 862 people to train computers to make more accurate predictions. They combined different computer learning methods to develop a new technique that can accurately estimate age and gender. The results show that this method is quite good, especially for estimating age. This research has the potential to help forensic investigators and anthropologists in their work. |
Keywords
» Artificial intelligence » Boosting » Extreme gradient boosting » Machine learning » Random forest
