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Objective <p>Sex determination is an important parameter for personal identification in forensic and medico-legal examinations. The study aims at predicting sex accuracy from different parameters of ear and nose by using a novel approach of Machine Learning Library, ‘PyCaret’.</p> Results <p>The present research was carried out on 508 participants (264 males and 244 females) aged 18–35 years from north India. Various ear and nose measurements were recorded on each participant. PyCaret employs a train-eval-testing validation approach, yielding a comprehensive output of the model in the form of a table that consolidates the average scores of all models over ten folds, including the respective time values. These models were compared based on performance metrics, and time taken. The logistic regression classifier emerged as the top-performing model, achieving the highest scores of 86.75% for sex prediction accuracy. Nasal breadth has been concluded as the most significant variable in accurate sex prediction. The findings indicate that the majority of the ear and nose characteristics significantly contribute to sexual dimorphism. This novel approach for sex classification can be efficiently used in a variety of forensic examinations and crime scene investigation especially where there is a need for estimation of sex for personal identification.</p>

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Sex classification accuracy through machine learning algorithms - morphometric variables of human ear and nose

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月15日

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  • Tej Kaur,
  • Kewal Krishan,
  • Akanksha Sharma,
  • Ankita Guleria,
  • Vishal Sharma

摘要

Objective

Sex determination is an important parameter for personal identification in forensic and medico-legal examinations. The study aims at predicting sex accuracy from different parameters of ear and nose by using a novel approach of Machine Learning Library, ‘PyCaret’.

Results

The present research was carried out on 508 participants (264 males and 244 females) aged 18–35 years from north India. Various ear and nose measurements were recorded on each participant. PyCaret employs a train-eval-testing validation approach, yielding a comprehensive output of the model in the form of a table that consolidates the average scores of all models over ten folds, including the respective time values. These models were compared based on performance metrics, and time taken. The logistic regression classifier emerged as the top-performing model, achieving the highest scores of 86.75% for sex prediction accuracy. Nasal breadth has been concluded as the most significant variable in accurate sex prediction. The findings indicate that the majority of the ear and nose characteristics significantly contribute to sexual dimorphism. This novel approach for sex classification can be efficiently used in a variety of forensic examinations and crime scene investigation especially where there is a need for estimation of sex for personal identification.