Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System
The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are requ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/2/245 |
| _version_ | 1797622267531755520 |
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| author | In-Ae Kang Soualihou Ngnamsie Njimbouom Jeong-Dong Kim |
| author_facet | In-Ae Kang Soualihou Ngnamsie Njimbouom Jeong-Dong Kim |
| author_sort | In-Ae Kang |
| collection | DOAJ |
| description | The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed method was compared to recently proposed dental procedures. Among these classifiers, the suggested GBDT trained with a reduced feature set achieved the best classification performance, with accuracy, F1-score, precision, and recall values of 95%, 93%, 99%, and 88%, respectively. Furthermore, the experimental results suggest that feature selection improved the performance of the various classifiers. The suggested method yielded a good predictive model for dental caries diagnosis, which might be used in more imbalanced medical datasets to identify disease more effectively. |
| first_indexed | 2024-03-11T09:07:50Z |
| format | Article |
| id | doaj.art-2afddd2c20264d0f913bc4419cb8a4d0 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T09:07:50Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-2afddd2c20264d0f913bc4419cb8a4d02023-11-16T19:11:42ZengMDPI AGBioengineering2306-53542023-02-0110224510.3390/bioengineering10020245Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support SystemIn-Ae Kang0Soualihou Ngnamsie Njimbouom1Jeong-Dong Kim2Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan-si 31460, Republic of KoreaDepartment of Computer and Electronics Convergence Engineering, Sun Moon University, Asan-si 31460, Republic of KoreaDepartment of Computer and Electronics Convergence Engineering, Sun Moon University, Asan-si 31460, Republic of KoreaThe high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed method was compared to recently proposed dental procedures. Among these classifiers, the suggested GBDT trained with a reduced feature set achieved the best classification performance, with accuracy, F1-score, precision, and recall values of 95%, 93%, 99%, and 88%, respectively. Furthermore, the experimental results suggest that feature selection improved the performance of the various classifiers. The suggested method yielded a good predictive model for dental caries diagnosis, which might be used in more imbalanced medical datasets to identify disease more effectively.https://www.mdpi.com/2306-5354/10/2/245disease dental cariesgradient boosting decision treefeature selectionmachine learningfeature importance |
| spellingShingle | In-Ae Kang Soualihou Ngnamsie Njimbouom Jeong-Dong Kim Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System Bioengineering disease dental caries gradient boosting decision tree feature selection machine learning feature importance |
| title | Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System |
| title_full | Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System |
| title_fullStr | Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System |
| title_full_unstemmed | Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System |
| title_short | Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System |
| title_sort | optimal feature selection based dental caries prediction model using machine learning for decision support system |
| topic | disease dental caries gradient boosting decision tree feature selection machine learning feature importance |
| url | https://www.mdpi.com/2306-5354/10/2/245 |
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