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

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dc.authoridyilmaz, nesibe/0000-0002-5527-8507
dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.contributor.authorYilmaz, Nesibe
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorMercan, Kadir
dc.date.accessioned2024-09-29T16:05:11Z
dc.date.available2024-09-29T16:05:11Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBackground 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.en_US
dc.identifier.doi10.1186/s41935-024-00400-6
dc.identifier.issn2090-536X
dc.identifier.issn2090-5939
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85195866626en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1186/s41935-024-00400-6
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6553
dc.identifier.volume14en_US
dc.identifier.wosWOS:001243985200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInt Assoc Law & Forensic Sciencesen_US
dc.relation.ispartofEgyptian Journal of Forensic Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSex estimationen_US
dc.subjectAge estimationen_US
dc.subjectEyeballen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectArtificial neural networksen_US
dc.titleSex and age estimation with corneal topography parameters by using machine learning algorithms and artificial neural networksen_US
dc.typeArticleen_US

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