Application of machine learning for sex estimation using the first and second ribs in the Northern Thai population.

Abstract

Sex estimation is a fundamental component of forensic biological profiling, yet primary markers such as the pelvis and skull are frequently fragmented or absent due to taphonomic factors. This study investigates the diagnostic efficacy of the first and second atypical ribs for sex estimation within a Northern Thai population using advanced machine learning (ML). Osteometric variables, 15 for the first rib and 14 for the second were recorded from 200 right side ribs (100 males, 100 females) and 177 left side ribs (100 males, 77 females) at Chiang Mai University. Twelve ML algorithms, including ensemble stacking and voting classifiers, were refined via recursive feature elimination with cross-validation (RFECV) and hyperparameter optimization through the Optuna framework. Results demonstrated pronounced sexual dimorphism, with male dimensions consistently exceeding those of females across nearly all variables (P<0.05). Optimized models exhibited superior predictive performance; specifically, quadratic discriminant analysis for the right first rib yielded a hold-out accuracy of 85%. Notably, the stacking model applied to the right second rib achieved an accuracy of 90%, equivalent to the performance of models utilizing both ribs combined. In conclusion, ML integrated rib morphometry provides a highly accurate and robust alternative for sex estimation in the Northern Thai population, particularly in cases where standard skeletal markers are compromised.