Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning
Extraction of teeth is an important treatment decision in orthodontic practice. An expert system that is able to arrive at suitable treatment decisions can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability. In this work...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/7/2/55 |
| _version_ | 1797565509833588736 |
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| author | Yasir Suhail Madhur Upadhyay Aditya Chhibber Kshitiz |
| author_facet | Yasir Suhail Madhur Upadhyay Aditya Chhibber Kshitiz |
| author_sort | Yasir Suhail |
| collection | DOAJ |
| description | Extraction of teeth is an important treatment decision in orthodontic practice. An expert system that is able to arrive at suitable treatment decisions can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability. In this work, we train a number of machine learning models for this prediction task using data for 287 patients, evaluated independently by five different orthodontists. We demonstrate why ensemble methods are particularly suited for this task. We evaluate the performance of the machine learning models and interpret the training behavior. We show that the results for our model are close to the level of agreement between different orthodontists. |
| first_indexed | 2024-03-10T19:14:11Z |
| format | Article |
| id | doaj.art-9704f2cbc3224fbf832687343e932ef7 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T19:14:11Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-9704f2cbc3224fbf832687343e932ef72023-11-20T03:34:33ZengMDPI AGBioengineering2306-53542020-06-01725510.3390/bioengineering7020055Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble LearningYasir Suhail0Madhur Upadhyay1Aditya Chhibber2Kshitiz3Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT 06032, USADivision of Orthodontics, School of Dental Medicine, University of Connecticut Health Center, Farmington, CT 06032, USAPrivate Practice, Norwalk, OH 44857, USADepartment of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT 06032, USAExtraction of teeth is an important treatment decision in orthodontic practice. An expert system that is able to arrive at suitable treatment decisions can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability. In this work, we train a number of machine learning models for this prediction task using data for 287 patients, evaluated independently by five different orthodontists. We demonstrate why ensemble methods are particularly suited for this task. We evaluate the performance of the machine learning models and interpret the training behavior. We show that the results for our model are close to the level of agreement between different orthodontists.https://www.mdpi.com/2306-5354/7/2/55orthodonticsneural networkmachine learningrandom forestsensemble methods |
| spellingShingle | Yasir Suhail Madhur Upadhyay Aditya Chhibber Kshitiz Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning Bioengineering orthodontics neural network machine learning random forests ensemble methods |
| title | Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning |
| title_full | Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning |
| title_fullStr | Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning |
| title_full_unstemmed | Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning |
| title_short | Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning |
| title_sort | machine learning for the diagnosis of orthodontic extractions a computational analysis using ensemble learning |
| topic | orthodontics neural network machine learning random forests ensemble methods |
| url | https://www.mdpi.com/2306-5354/7/2/55 |
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