Ahmet DEPRELI, Mustafa Furkan OZTURK, Ömer Faruk NASIP, Betul SEVINDIK
Annals of Clinical and Analytical Medicine - 2026;17(7):742-746
Aim: The identification of human remains resulting from mass disasters, explosive-induced demolitions, or attacks constitutes one of the most critical areas of forensic medicine. The aim of this study is to enable high-accuracy sex prediction from foot morphometric measurements, employing both classical statistical analyses and machine learning algorithms. Methods: A total of 1000 individuals, 500 females and 500 males, aged 18-36, were included in the study. Parameters included foot length, foot metatarsal width, foot calcaneal width, medial and lateral malleolus distance to the ground, unweighted navicular height, and ASIS-heel distance. The Mann-Whitney U test was used to assess differences between genders, and prediction models were developed using logistic regression and various machine learning algorithms. Results: Foot length/height, ASIS-heel distance/height, and foot length/ASIS-heel distance ratios were the strongest predictors of gender differentiation. Machine learning models achieved very high accuracy. Random Forest and SVM achieved near-perfect classification performance, while the Logistic Regression model achieved 99% accuracy in predicting gender. Decision Tree model achieved 98.4% accuracy. Feature importance analysis indicated that the foot length/height, ASIS-heel distance/height, and foot length/ASIS-heel distance ratios were the most critical parameters for gender prediction. Conclusion: Foot morphometric measurements provide high accuracy for sex estimation when used with both traditional statistical methods and machine learning algorithms. The results demonstrate that biometric sex estimation based on foot morphology can be used as a practical and effective method in fields such as forensic science and sports science.
