Development of artificial neural network model for predicting the rapid maxillary expansion technique in children with cleft lip and palate
The effectiveness of the hybrid biometric model created in this work, which combines bootstrap and BLR with R-syntax was evaluated in terms of how accurately it predicted a binary response variable. A validation method based on an MLFFNN was used to evaluate the precision of the generated model. This leads to a good outcome.
Authors
- Matheel AL-Rawas (ORCID: https://orcid.org/0000-0001-6919-5334)
- Siddharthan Selvaraj (ORCID: https://orcid.org/0000-0002-7776-3335)
- Nafij Bin Jamayet (ORCID: https://orcid.org/0000-0003-4656-0946)
- Mohammad Khursheed Alam (ORCID: https://orcid.org/0000-0001-7131-1752)
- Abedelmalek Kalefh Tabnjh (ORCID: https://orcid.org/0000-0001-8263-4944)
- Johari Yap Abdullah (ORCID: https://orcid.org/0000-0002-6147-4192)
- Adam Husein
- Mohamed Zahoor Ul Huqh (ORCID: https://orcid.org/0000-0002-4030-2009)
- Wan Muhamad Amir W Ahmad (ORCID: https://orcid.org/0000-0003-2366-3918)
- Mohd Rosli bin Yahya
Institutions
- Jordan University of Science and Technology (JO)
- Jouf University (SA)
- University of Sharjah (AE)
- Hospital Universiti Sains Malaysia (MY)
- Dr. D.Y. Patil Vidyapeeth, Pune (IN)
- Hospital Raja Perempuan Zainab II (MY)
- Dr D Y Patil Dental College & Hospital (IN)
- IMU University (MY)
- Saveetha University (IN)
- University of Gothenburg (SE)
- SEGi University (MY)
Publication Details
- Journal
- Frontiers in Dental Medicine
- Published
- 2025-04-15
- DOI
- https://doi.org/10.3389/fdmed.2025.1530372
- Primary Topic
- Cleft Lip and Palate Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00