Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate
Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP an...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/19/3025 |
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| author | Mohamed Zahoor Ul Huqh Johari Yap Abdullah Matheel AL-Rawas Adam Husein Wan Muhamad Amir W Ahmad Nafij Bin Jamayet Maya Genisa Mohd Rosli Bin Yahya |
| author_facet | Mohamed Zahoor Ul Huqh Johari Yap Abdullah Matheel AL-Rawas Adam Husein Wan Muhamad Amir W Ahmad Nafij Bin Jamayet Maya Genisa Mohd Rosli Bin Yahya |
| author_sort | Mohamed Zahoor Ul Huqh |
| collection | DOAJ |
| description | Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. Methods: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. Results: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study’s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. Conclusion: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern. |
| first_indexed | 2024-03-10T21:47:02Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-10T21:47:02Z |
| publishDate | 2023-09-01 |
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| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c8d844c1e65447fca383e273f3d7e4872023-11-19T14:13:41ZengMDPI AGDiagnostics2075-44182023-09-011319302510.3390/diagnostics13193025Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and PalateMohamed Zahoor Ul Huqh0Johari Yap Abdullah1Matheel AL-Rawas2Adam Husein3Wan Muhamad Amir W Ahmad4Nafij Bin Jamayet5Maya Genisa6Mohd Rosli Bin Yahya7Orthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, MalaysiaCraniofacial Imaging Lab, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, MalaysiaProsthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, MalaysiaProsthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, MalaysiaDepartment of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, MalaysiaDivision of Restorative Dentistry (Prosthodontics), School of Dentistry, International Medical University, Bukit Jalil, Kuala Lumpur 57000, MalaysiaBiomedical Programme, Faculty of Pascasarjana, YARSI University, Jakarta 10510, IndonesiaOral & Maxillofacial Department, Hospital Raja Perempuan Zainab II, Kota Bharu 15586, MalaysiaIntroduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. Methods: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. Results: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study’s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. Conclusion: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.https://www.mdpi.com/2075-4418/13/19/3025unilateral cleft lip and palateartificial neural networklogistic regressionmaxillary archnon-syndromic cleft |
| spellingShingle | Mohamed Zahoor Ul Huqh Johari Yap Abdullah Matheel AL-Rawas Adam Husein Wan Muhamad Amir W Ahmad Nafij Bin Jamayet Maya Genisa Mohd Rosli Bin Yahya Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate Diagnostics unilateral cleft lip and palate artificial neural network logistic regression maxillary arch non-syndromic cleft |
| title | Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate |
| title_full | Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate |
| title_fullStr | Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate |
| title_full_unstemmed | Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate |
| title_short | Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate |
| title_sort | development of artificial neural network based prediction model for evaluation of maxillary arch growth in children with complete unilateral cleft lip and palate |
| topic | unilateral cleft lip and palate artificial neural network logistic regression maxillary arch non-syndromic cleft |
| url | https://www.mdpi.com/2075-4418/13/19/3025 |
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