Evaluation of Anthropometric Measurement Results and the Relationship Between Individual Identity and Geographic Belonging Through Artificial Neural Networks from a Mental Health Perspective.

Abstract

Background: Identity verification and geographical belonging are significant issues with mental health implications, particularly in forensic contexts. Anthropometric measurements offer potential insights into these relationships.

Aim: This study aims to evaluate the significance of anthropometric measurement results and the relationship between an individual's identity and their geographical belonging through artificial neural networks from a mental health perspective.

Methods: Study Population: The study population consisted of female individuals who visited or were brought to the forensic medicine outpatient clinic of a public hospital located in the center of Balıkesir Province between June 2023 and October 2023. Sample: The sample consisted of 100 voluntary female participants who agreed to take part in the study. The participants' geographical origins were inquired, and anthropometric measurements were conducted. Measurement results were recorded in an artificial neural network program using participant code names and evaluated using the Matlab program.

Results: It was found that lip prints, fingerprints, and the angle of the mandible contained varying amounts of usable data in both the training and testing phases. The system developed by the researchers achieved a high success rate with an R-value of 1 during the training process and 0.97 during the testing process.

Conclusion: In future research addressing identity verification as a social issue from a mental health perspective, solutions may involve improving the performance of this system by utilizing different artificial neural network models, learning algorithms, and activation functions.