Authors
Alah Dawood Aldawoody, Shehab Ahmed Hamad
Published in
Journal of clinical and experimental dentistry. Volume 18. Issue 8. Pages e990-e997. Epub Jul 29, 2026.
Abstract
Orthodontic extraction decision-making remains difficult and highly subjective, particularly in marginal cases where the clinicalcues are ambiguous. Objectives: To design machine learning (ML) models for prediction of extraction vs. non-extraction decision-making and estimate the influenceof key clinical predictors on such decisions.
Retrospective analysis was performed on 120 patients with extraction and 80 patients without extraction from asample of pretreatment records over 2 years. Five ML models including Logistic Regression (LR), Random Forest (RF), Support VectorMachine (SVM), Decision Tree (DT) and XGBoost are employed in this research by applying Python's Scikit-learn. The datasetwas divided in two parts for training and testing at a ratio of 70:30. The sensitivity, specificity, accuracy and AUC-ROCwere used to evaluate and compare the performance of the models. In order to rank the most important features for decision-making, feature importance was calculated.
RF model provided the highest accuracy (93.5%) and AUC-ROC (0.95) values, whereas XGBoost was the second-bestmodel, with accuracy (90.2%) and AUC-ROC (0.92). Mandibular crowding (weight = 0.28) and IMPA (L1-MP angle,weight = 0.22) were the most influential predictors.
Ensemble ML models, in particular RF, yield a promising objective methodology for clinical decision support in orthodontics topotentially lessen inter-clinician variation and enhance consistency in treatment planning.
PMID:
42542790
Bibliographic data and abstract were imported from PubMed on 02 Aug 2026.
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