Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study
Background and Aim: Traumatic dental injuries (TDIs) have become the public dental health problem worldwide in children and adolescents. These injuries are complex and multifactorial in aetiology. This study was done with the aim to analyse the association of 'type of TDI' with its demogra...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-04-01
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| Series: | Indian Journal of Dental Research |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/ijdr.ijdr_846_21 |
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| _version_ | 1823863774131519488 |
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| author | Mohammad Kamran Khan Mahendra Kumar Jindal |
| author_facet | Mohammad Kamran Khan Mahendra Kumar Jindal |
| author_sort | Mohammad Kamran Khan |
| collection | DOAJ |
| description | Background and Aim:
Traumatic dental injuries (TDIs) have become the public dental health problem worldwide in children and adolescents. These injuries are complex and multifactorial in aetiology. This study was done with the aim to analyse the association of 'type of TDI' with its demographic and various predisposing factors in children by an advanced statistical method of machine learning (ML) of artificial intelligence (AI).
Materials and Methods:
The present study's data were gathered by conducting the observational cross-sectional study among index age-groups 12 and 15 years children of randomly selected schools of different geographical regions. Structured interviews and dental examinations performed were done to record the variables of TDIs in self-constructed proforma. The gathered data were analysed by employing the random-tree model of machine learning algorithm of IBM SPSS Modeler version-18 software.
Results:
Molar-relationship (2.5), age (1.75), sex (1.5) and geographical region/area (~1.5) were the most important predictors (factors) for the determination of type of dental injury as shown by the random tree model, whereas clinical factors like overjet (0.75), lip-competence (0.5) and overbite (0.5) showed lesser importance in the determination of type of TDIs.
Conclusion:
Demographic factors (age, sex and geographical region) and one clinical factor (molar-relation) were found as the stronger factors for determining the type of traumatic dental injury in children. |
| format | Article |
| id | doaj-art-87f56d04e65f4e92b71a45f3cf371417 |
| institution | Kabale University |
| issn | 0970-9290 1998-3603 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Indian Journal of Dental Research |
| spelling | doaj-art-87f56d04e65f4e92b71a45f3cf3714172025-02-09T09:36:18ZengWolters Kluwer Medknow PublicationsIndian Journal of Dental Research0970-92901998-36032023-04-0134211411810.4103/ijdr.ijdr_846_21Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional StudyMohammad Kamran KhanMahendra Kumar JindalBackground and Aim: Traumatic dental injuries (TDIs) have become the public dental health problem worldwide in children and adolescents. These injuries are complex and multifactorial in aetiology. This study was done with the aim to analyse the association of 'type of TDI' with its demographic and various predisposing factors in children by an advanced statistical method of machine learning (ML) of artificial intelligence (AI). Materials and Methods: The present study's data were gathered by conducting the observational cross-sectional study among index age-groups 12 and 15 years children of randomly selected schools of different geographical regions. Structured interviews and dental examinations performed were done to record the variables of TDIs in self-constructed proforma. The gathered data were analysed by employing the random-tree model of machine learning algorithm of IBM SPSS Modeler version-18 software. Results: Molar-relationship (2.5), age (1.75), sex (1.5) and geographical region/area (~1.5) were the most important predictors (factors) for the determination of type of dental injury as shown by the random tree model, whereas clinical factors like overjet (0.75), lip-competence (0.5) and overbite (0.5) showed lesser importance in the determination of type of TDIs. Conclusion: Demographic factors (age, sex and geographical region) and one clinical factor (molar-relation) were found as the stronger factors for determining the type of traumatic dental injury in children.https://journals.lww.com/10.4103/ijdr.ijdr_846_21artificial intelligence (ai)machine learning (ml)predisposing factorsrandom tree classifiertype of dental trauma |
| spellingShingle | Mohammad Kamran Khan Mahendra Kumar Jindal Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study Indian Journal of Dental Research artificial intelligence (ai) machine learning (ml) predisposing factors random tree classifier type of dental trauma |
| title | Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study |
| title_full | Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study |
| title_fullStr | Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study |
| title_full_unstemmed | Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study |
| title_short | Random Tree Algorithm to Analyse the Relation between Type of Traumatic Dental Injuries and Its Demographic and Predisposing Factors - A Cross-Sectional Study |
| title_sort | random tree algorithm to analyse the relation between type of traumatic dental injuries and its demographic and predisposing factors a cross sectional study |
| topic | artificial intelligence (ai) machine learning (ml) predisposing factors random tree classifier type of dental trauma |
| url | https://journals.lww.com/10.4103/ijdr.ijdr_846_21 |
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