Talk · November 4, 2026

Predicting Facial Soft Tissue Thickness from Skeletal Malocclusion Class and Statistically Engineered Features Using Machine Learning in Cambodian Adults

Haksrun Lao, Ruslan Isaev, Anand Marya

Abstract

This paper presents a two-experiment study aimed at predicting facial soft tissue thickness (FSTT) at 11 anatomical landmarks in Cambodian adults based on skeletal malocclusion class and sex. Experiment 1 (preliminary) assesses three multi-output regressors—Linear Regression (LR), Random Forest (RF), and Gradient Boosting (GB)—utilizing four baseline features and 70 engineered features derived from landmarks across seven categories of statistical transformations. Baseline models achieve a test mean absolute error (MAE) of 1.506–1.527 mm (R² = −0.002 to 0.026), whereas engineered models attain near-perfect R² (≥0.987), identified as structural target leakage: z-score and class-deviation features deterministically encode the prediction targets. Experiment 2 (systematic three-phase) addresses the leakage issue through a leakage-free feature design. Phase A reinstates the baseline (MAE 1.427–1.453 mm); Phase B broadens inputs to 16 valid class-sex interaction features across three output strategies; Phase C assesses Regressor Chains through four landmark orderings (anatomical, reverse, variance-based, and data-driven optimal) for a total of 12 configurations. Neither feature engineering (∆MAE = −0.004 mm avg.) nor chain ordering (detrimental to RF and GB; neutral for LR) produces significant enhancement. LR attains the optimal outcome in both experiments (Phase A test MAE = 1.4266 mm, R² = 0.047). Kruskal-Wallis analysis indicates Stomion and Labrale superius as the most discriminative landmarks for class differentiation. Engineered interaction features (Class_ordinal_Sex: 15.9%; ClassIII_Male: 13.1%) predominate in feature importance in Experiment 2, yet yield no predictive advantage. These results validate a performance ceiling dictated by significant intra-class FSTT variability, irrespective of feature engineering method or output structure.

Orthodontics and Dentofacial OrthopedicsForensic Anthropology and Bioarchaeology StudiesDental Radiography and Imagingfacial soft tissue thicknessfeature engineeringtarget leakage

Predicting Facial Soft Tissue Thickness from Skeletal Malocclusion Class and Statistically Engineered Features Using Machine Learning in Cambodian Adults

Abstract

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