ORIGINAL RESEARCH article
Front. Oral Health
Sec. Oral and Maxillofacial Surgery
A Hybrid Approach to Predicting and classifying Dental Impaction: Integrating Regularized Regression and XG boost methods
- AM
Asok mathew 1
- PK
Pradeep kumar yadalam 1
- AR
Ahmed Radeideh 2
- SH
Shorouq Hadi 3
- RS
Rona swed 3
- RC
Reyyan cheema 3
- MM
Majd Mousa AL-Mohammad 3
- MA
Mohammed Alsaegh 3
- SR
Shishir Ram Shetty 4
1. Saveetha Dental College And Hospitals, Chennai, India
2. University of Science and Technology of Fujairah, Fujairah, United Arab Emirates
3. Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates, Ajman
4. University of Sharjah, Sharjah, United Arab Emirates, 27272
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Abstract
Scope Statement:** This article explores a hybrid approach to predicting and classifying dental impaction using a combination of regularized regression and XGBoost techniques. By integrating these advanced predictive models, the study aims to improve the accuracy and efficiency of impaction diagnosis in oral and maxillofacial contexts. This approach is assessed for its potential to assist clinicians in early detection and management of impacted teeth, thereby enhancing treatment planning and patient outcomes. The findings could contribute significantly to computational methodologies in dental and maxillofacial research, aligning with the journal's focus on innovative, data-driven diagnostic solutions.
Summary
Keywords
Conceptualization, Data curation, Formal analysis, methodology, Writing -original draft, Writing -review & editing. Asok mathew: Conceptualization, investigation, project administration
Received
07 November 2024
Accepted
28 March 2025
Copyright
© 2025 mathew, yadalam, Radeideh, Hadi, swed, cheema, Mousa AL-Mohammad, Alsaegh and Ram Shetty. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Pradeep kumar yadalam, pradeepkumar.sdc@saveetha.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.