Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns

• Published in: BMC oral health Vol. 25; no. 1; pp. 1246 - 10
• Main Authors: Gonca, Merve, Özel, Mehmet Birol
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.07.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adolescent; Algorithms; Cephalometry; Cephalometry - methods; Child; Classification; Cluster Analysis; Clustering; Data mining; Datasets; Decision Trees; Dentistry; Female; Females; Health aspects; Humans; Hypotheses; Machine Learning; Male; Malocclusion - classification; Malocclusion - diagnostic imaging; Malocclusion - pathology; Mandible; Medicine; Oral and Maxillofacial Surgery; Orthodontics; Phenotypes; Phenotyping; Practice; Software; Statistical analysis; Technology application; Variance analysis; Turkey; Cephalometry; Algorithms; Orthodontics; Machine learning
• ISSN: 1472-6831; 1472-6831
• DOI: 10.1186/s12903-025-06651-6

Background Craniofacial phenotyping is essential for individualized orthodontic diagnosis and treatment planning. Traditional skeletal classifications, such as the ANB angle, may oversimplify complex relationships among malocclusion types. Machine learning-based unsupervised methods may allow for more nuanced sub-phenotypic classification. Methods A total of 330 pre-treatment LCRs (110 each from Class 1, Class 2, and Class 3 based on ANB (°)) were assessed in this study. The X-means method was used to create clusters. The relationship between the clusters and cephalometric variables was evaluated using the C4.5 decision tree. X-means clustering was employed to identify natural groupings within the dataset, followed by C4.5 decision tree analysis to determine key discriminative variables. After post-pruning, 288 LCRs were included in the final analysis. One-way ANOVA and Kruskal-Wallis tests were used to assess differences among clusters. Results A total of four clusters were obtained using the X-means algorithm. Decision trees were used to identify the most discriminative variables among clusters. These clusters exhibited distinctive sagittal and vertical skeletal and dental features, particularly differences in individualized ANB, interincisal angle, and mandibular plane inclination. The root node in the second decision tree was the Individualized ANB (°). The interincisal angle was the main parameter determining the distinction between Clusters 0 and 1. The main parameter that determined the distinction between Cluster 2 and Cluster 3 was N-Go-Gn (°). Significant differences were found in all measurements except N-Go-Ar (°), FH/PP (°), and S-Ar-Go (°) angles ( p  < 0.05). Conclusion The combination of X-means clustering and C4.5 decision tree analysis enabled the identification of four distinct craniofacial sub-phenotypes across all skeletal malocclusion classes. Four sub-phenotypic categorizations of all skeletal malocclusions were obtained. Mandibular plane inclination and interincisal angle were the most critical variables distinguishing these phenotypes. Assessing various forms of skeletal malocclusions may improve clinical outcomes and diagnostics by showing how different skeletal classes interact.