Comparison of Gender Prediction Accuracy between Regression Models Derived from Hand, Foot Measurements and Long Bone Measurements in a Sample of Kolhapur Population

Abstract

Introduction: Here, we aim to compare the accuracy of the regression formula derived to predict gender
using data on measurements of foot and hand with the formula derived to predict gender using the length
measurements data of the long bones (tibia and ulna).
Methods: Patients attending the outpatient services at the Orthopedic Department, and between the age
range 18 to 50 years were recruited (n=1000; 500 males and 500 females). Subjects suffering from any kind
of bone deformity were excluded. Vallois method was used to estimate the measurements of hand, foot,
tibia, and ulna. Regression formulas were obtained from the hand, foot-long bones measurements; to predict
gender, using multiple logistic regression.
Results: Differences between male’s and female’s measurements of hand (P<0.001), long bones (P<0.001),
and foot (P<0.001) were significant. The accuracy of the model used to predict gender, which was calculated
from the dimensions of hand and foot was 81.5%. The accuracy of the model used to predict gender calculated
from the long bone measurement was 78.3%.
Conclusions: Dimensions of hand and foot are a better predictor (81.5%) of gender vs the length of long
bones (tibia and ulna) (78.3%) in the Kolhapur population.