Sex and age estimation with corneal topography parameters by using machine learning algorithms and artificial neural networks

Abstract

Abstract Background The aim of this study, which was based on this hypothesis, was to estimate sex and age by using a machine learning algorithm (ML) and artificial neural networks (ANN) with parameters obtained from the eyeball. The study was conducted on corneal topography images of 155 women and 155 men aged between 6 and 87 who did not have surgical intervention or pathology in their eyeballs. In the study, the individuals were divided into four different age groups 6–17, 18–34, 35–55, and 56–87. Sex and age estimation was carried out by using the numerical data of parameters obtained as a result of corneal topography imaging in ML and ANN inputs. Results As a result of our study, in sex determination, a 0.98 accuracy rate (Acc) was obtained with the logistic regression algorithm, one of the ML algorithms, and 0.94 Acc was obtained with the MLCP model, one of the ANN algorithms; in age estimation, 0.84 Acc was obtained with RF algorithm, one of the ML algorithms. With the SHAP analyzer of the Random Forest algorithm, through which the effects of parameters on the overall result are evaluated, the parameter that made the highest contribution to sex estimation was found to be corneal volume, and the parameter that made the highest contribution to age estimation was found to be pupil Q parameter. Conclusion As a result of our study, it was found that parameters obtained from the eyeball showed a high accuracy in sex and age estimation.

Keywords

• Sex estimation
• Age estimation
• Eyeball
• Machine learning algorithms
• Artificial neural networks