A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques
Introduction. Early childhood caries (ECC) is a prevalent dental condition that significantly impacts children’s quality of life and is influenced by both environmental and metabolic factors. Anthropometric variables such as weight, height, and body mass index serve as indicators of overall growth a...
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Ivano-Frankivsk National Medical University
2025-03-01
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| Series: | Galician Medical Journal |
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| Online Access: | https://ifnmujournal.com/gmj/article/view/2058 |
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| author | Fei Hong Seng Wan Muhamad Amir W Ahmad Mohamad Nasarudin Adnan Farah Muna Mohamad Ghazali Nor Azlida Aleng |
| author_facet | Fei Hong Seng Wan Muhamad Amir W Ahmad Mohamad Nasarudin Adnan Farah Muna Mohamad Ghazali Nor Azlida Aleng |
| author_sort | Fei Hong Seng |
| collection | DOAJ |
| description | Introduction. Early childhood caries (ECC) is a prevalent dental condition that significantly impacts children’s quality of life and is influenced by both environmental and metabolic factors. Anthropometric variables such as weight, height, and body mass index serve as indicators of overall growth and nutritional status, which are closely linked to oral health. Additionally, family size may play a role in ECC risk by influencing dietary habits and oral hygiene practices. Despite these associations, the complex and nonlinear relationships between these factors and ECC risk remain insufficiently explored.
This study aims to investigate the potential relationships between ECC risk and a combination of social (family size) and anthropometric parameters (weight, height, and body mass index) by employing nonparametric regression and validating these relationships with a multilayer feed-forward neural network (MLFNN).
Methods. This cross-sectional observational study utilized secondary data from Universiti Sains Malaysia Hospital, Kota Bharu, Kelantan, Malaysia. The dataset was divided into training (60%), testing (30%), and validation (10%) subsets. A generalized additive model (GAM) was used to capture nonlinear relationships, followed by MLFNN validation. Model performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), median absolute error (MedAE), and mean squared error (MSE).
Results. The dataset exhibited non-normality, justifying the use of nonparametric regression. In the GAM, only weight showed a nonlinear relationship, with no other predictors significantly influencing ECC risk, except for the intercept. The MLFNN achieved an error value of 0.089 and an accuracy of 91.09%, with height contributing the most to ECC risk estimation.
Conclusions. Integrating nonparametric regression and MLFNN validation provides a robust framework for modelling ECC risk, capturing complex nonlinear relationships between family size and anthropometric factors. Height emerged as the most influential predictor, highlighting its association with growth and systemic health, followed by weight, body mass index, and family size. These findings underscore the need for a multifactorial approach in ECC prevention, emphasizing nutritional and family-related factors in pediatric dental care. |
| format | Article |
| id | doaj-art-ad366d564184453aaeb1891c1b141bca |
| institution | OA Journals |
| issn | 2414-1518 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ivano-Frankivsk National Medical University |
| record_format | Article |
| series | Galician Medical Journal |
| spelling | doaj-art-ad366d564184453aaeb1891c1b141bca2025-08-20T02:11:18ZengIvano-Frankivsk National Medical UniversityGalician Medical Journal2414-15182025-03-0132110.21802/e-GMJ2025-A062058A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN TechniquesFei Hong Seng0Wan Muhamad Amir W Ahmad1Mohamad Nasarudin Adnan2Farah Muna Mohamad Ghazali3Nor Azlida Aleng4School of Dental Sciences, Health Campus, Universiti Sains Malaysia, MalaysiaSchool of Dental Sciences, Health Campus, Universiti Sains Malaysia, MalaysiaSchool of Dental Sciences, Health Campus, Universiti Sains Malaysia, MalaysiaSchool of Dental Sciences, Health Campus, Universiti Sains Malaysia, MalaysiaFaculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, MalaysiaIntroduction. Early childhood caries (ECC) is a prevalent dental condition that significantly impacts children’s quality of life and is influenced by both environmental and metabolic factors. Anthropometric variables such as weight, height, and body mass index serve as indicators of overall growth and nutritional status, which are closely linked to oral health. Additionally, family size may play a role in ECC risk by influencing dietary habits and oral hygiene practices. Despite these associations, the complex and nonlinear relationships between these factors and ECC risk remain insufficiently explored. This study aims to investigate the potential relationships between ECC risk and a combination of social (family size) and anthropometric parameters (weight, height, and body mass index) by employing nonparametric regression and validating these relationships with a multilayer feed-forward neural network (MLFNN). Methods. This cross-sectional observational study utilized secondary data from Universiti Sains Malaysia Hospital, Kota Bharu, Kelantan, Malaysia. The dataset was divided into training (60%), testing (30%), and validation (10%) subsets. A generalized additive model (GAM) was used to capture nonlinear relationships, followed by MLFNN validation. Model performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), median absolute error (MedAE), and mean squared error (MSE). Results. The dataset exhibited non-normality, justifying the use of nonparametric regression. In the GAM, only weight showed a nonlinear relationship, with no other predictors significantly influencing ECC risk, except for the intercept. The MLFNN achieved an error value of 0.089 and an accuracy of 91.09%, with height contributing the most to ECC risk estimation. Conclusions. Integrating nonparametric regression and MLFNN validation provides a robust framework for modelling ECC risk, capturing complex nonlinear relationships between family size and anthropometric factors. Height emerged as the most influential predictor, highlighting its association with growth and systemic health, followed by weight, body mass index, and family size. These findings underscore the need for a multifactorial approach in ECC prevention, emphasizing nutritional and family-related factors in pediatric dental care.https://ifnmujournal.com/gmj/article/view/2058nonparametric regressiongeneralized additive modelmultilayer feed-forward neural networkearly childhood cariesfamily sizeweightheightbody mass index |
| spellingShingle | Fei Hong Seng Wan Muhamad Amir W Ahmad Mohamad Nasarudin Adnan Farah Muna Mohamad Ghazali Nor Azlida Aleng A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques Galician Medical Journal nonparametric regression generalized additive model multilayer feed-forward neural network early childhood caries family size weight height body mass index |
| title | A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques |
| title_full | A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques |
| title_fullStr | A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques |
| title_full_unstemmed | A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques |
| title_short | A Hybrid Approach to Modelling ECC Risk: Effectiveness of Nonparametric Regression and MLFNN Techniques |
| title_sort | hybrid approach to modelling ecc risk effectiveness of nonparametric regression and mlfnn techniques |
| topic | nonparametric regression generalized additive model multilayer feed-forward neural network early childhood caries family size weight height body mass index |
| url | https://ifnmujournal.com/gmj/article/view/2058 |
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