Abstract
Background: Estimating stature is of exceptional value in personal identification and the forensic sciences. At the same time, the literature lacks standardized methods for height estimation. Objectives: To estimate the stature of Iraqi individuals based on the morphometric parameters of both hands. Methods: 101 students recruited from Al-Rafidain University College collected data concerning ethnicity, gender, weight, and height and measured fourteen morphometric parameters for each hand, including the hand length (RHL; LHL), palm length (RPL; LPL), hand breadth (RHB; LHB), maximum handbreadth (RHB; LHB), and the length of each finger (RD1,2,3,4,5; LD1,2,3,4,5). A multivariable linear regression is used to predict stature based on the former variables. Results: The sample included males (48.5%) and females (51.5%) aged 18–23 years of Arabic (95%) and Kurdish (5%) ethnicity. The average weight (65.68±1.32) and height (169.52±1.13) were calculated. Regression analysis (accuracy=74.8%, p<0.001) and the male cohort (65%, p<0.001) possessed higher statistical accuracy than females (47.2%, p<0.001). Stature estimation within the total sample and males assumed similarities concerning parameters allocated to both hands that principally included the length of the right hand, right middle finger, left palm, and left little finger. In contrast, females' stature estimation corresponded to morphometric parameters strictly related to the left hand, including the length of the hand, palm, ring finger, and little finger. The results also aligned with the neural network analyses and qualitative data from ChatGPT version 3.5. Conclusion: Multivariable predictive models that are crucial in personal identification for forensic purposes were predicted, such as catastrophic scenarios due to disfiguring accidents, organized crime, and mass graves that frequently occurred in Iraq during the past two decades. Our results could also interest reconstructive hand surgery experts.
Publisher
Al-Rafidain University College
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