Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

Abstract

The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to different neck regions may vary depending on it. Recently, machine learning models (MLm) were used to classify knot position through these fractures. The contribution of aging on the fracture susceptibility is better demonstrated, but data on body weight (BW) and height (BH) significance on this is more doubtful and MLm did not consider them. A retrospectively obtained autopsy data on sex, age, BW, BH and distribution of greater hyoid bone horn (GHH), superior thyroid cartilage horn (STH), and cervical spine fractures in 368 suicidal hangings were analyzed by standard statistics to determine association of the anthropometrics (age, BW, and BH) with the fracture occurrence, and by machine learning algorithms to determine if body weight and height improved MLm classification of hanging cases with typical and atypical knot positions. In the sample, unilateral GHH fracture was significantly more common in atypical hangings, while isolated STH fractures were more common in typical hangings. Age was a predictor of GHH fractures and BW of STH fractures, but BW poorly correlated with their number. BH was not a predictor of any thyrohyoid fracture. On the ROC curve analysis, the MLm that considered BW and BH did not perform statistically better than MLm that did not consider them. The study indicates that body weight and height are of no detrimental value in assessing the thyrohyoid and cervical spine fracture patterns in suicidal hangings.