Sex and stature estimation from anthropometric measurements of the foot: linear analyses and neural network approach on a Turkish sample

IF 1.3 Q3 MEDICINE, LEGAL
Muhammed Emin Parlak, Bengü Berrak Özkul, Mucahit Oruç, Osman Celbiş
{"title":"Sex and stature estimation from anthropometric measurements of the foot: linear analyses and neural network approach on a Turkish sample","authors":"Muhammed Emin Parlak, Bengü Berrak Özkul, Mucahit Oruç, Osman Celbiş","doi":"10.1186/s41935-024-00391-4","DOIUrl":null,"url":null,"abstract":"For over a century, anthropometric techniques, widely used by anthropologists and adopted by medical scientists, have been utilized for predicting stature and sex. This study, conducted on a Eastern Turkish sample, aims to predict sex and stature using foot measurements through linear methods and Artificial Neural Networks. Our research was conducted on 134 medical students, comprising 69 males and 65 females. Stature and weight were measured in a standard anatomical position in the Frankfurt Horizontal Plane with a stadiometer of 0.1 cm precision. Measurements of both feet's height, length, and breadth were taken using a Vernier caliper, osteometric board, and height scale. The data were analyzed using SPSS 26.00. It was observed that all foot dimensions in males were significantly larger than in females. Sex prediction using linear methods yielded an accuracy of 94.8%, with a stature estimation error of 4.15 cm. When employing Artificial Neural Networks, sex prediction accuracy increased to 97.8%, and the error in stature estimation was reduced to 4.07 cm. Our findings indicate that Artificial Neural Networks can work more effectively with such data. Using Artificial Neural Networks, the accuracy of sex prediction for both feet exceeded 95%. Additionally, the error in stature estimation was reduced compared to the formulas obtained through linear methods.","PeriodicalId":11507,"journal":{"name":"Egyptian journal of forensic sciences","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Egyptian journal of forensic sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41935-024-00391-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
引用次数: 0

Abstract

For over a century, anthropometric techniques, widely used by anthropologists and adopted by medical scientists, have been utilized for predicting stature and sex. This study, conducted on a Eastern Turkish sample, aims to predict sex and stature using foot measurements through linear methods and Artificial Neural Networks. Our research was conducted on 134 medical students, comprising 69 males and 65 females. Stature and weight were measured in a standard anatomical position in the Frankfurt Horizontal Plane with a stadiometer of 0.1 cm precision. Measurements of both feet's height, length, and breadth were taken using a Vernier caliper, osteometric board, and height scale. The data were analyzed using SPSS 26.00. It was observed that all foot dimensions in males were significantly larger than in females. Sex prediction using linear methods yielded an accuracy of 94.8%, with a stature estimation error of 4.15 cm. When employing Artificial Neural Networks, sex prediction accuracy increased to 97.8%, and the error in stature estimation was reduced to 4.07 cm. Our findings indicate that Artificial Neural Networks can work more effectively with such data. Using Artificial Neural Networks, the accuracy of sex prediction for both feet exceeded 95%. Additionally, the error in stature estimation was reduced compared to the formulas obtained through linear methods.
根据足部人体测量数据估测性别和身材:土耳其样本的线性分析和神经网络方法
一个多世纪以来,人类学家广泛使用并被医学家采纳的人体测量技术一直被用于预测身材和性别。本研究以土耳其东部样本为对象,旨在通过线性方法和人工神经网络,利用脚部测量结果预测性别和身材。我们的研究对象是 134 名医科学生,其中包括 69 名男生和 65 名女生。在法兰克福水平面的标准解剖位置,用精度为 0.1 厘米的测力计测量身材和体重。使用游标卡尺、测骨板和身高标尺测量双脚的高度、长度和宽度。数据使用 SPSS 26.00 进行分析。结果表明,男性脚的所有尺寸都明显大于女性。使用线性方法预测性别的准确率为 94.8%,身材估计误差为 4.15 厘米。采用人工神经网络后,性别预测准确率提高到 97.8%,身材估计误差减少到 4.07 厘米。我们的研究结果表明,人工神经网络可以更有效地处理此类数据。使用人工神经网络,双脚的性别预测准确率超过了 95%。此外,与通过线性方法获得的公式相比,身材估计的误差也有所减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.00
自引率
0.00%
发文量
51
审稿时长
17 weeks
期刊介绍: Egyptian Journal of Forensic Sciences, the official publication of The International Association of Law and Forensic Sciences (IALFS), is an open access journal that publishes articles in the forensic sciences, pathology and clinical forensic medicine and its related specialities. The journal carries classic reviews, case studies, original research, hypotheses and learning points, offering critical analysis and scientific appraisal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信