Gender classification from anthropometric measurement by

J Natl Med Assoc · Jun 2023

Gender classification from anthropometric measurement by boosting decision tree: A novel machine learning approach.

The decision tree used a generating set of rules based on various correlated variables for developing an algorithm from the target variable. Using the training dataset this paper used boosting tree algorithm for gender classification from twenty-five anthropometric measurements and extract twelve significant variables chest diameter, waist girth, biacromial, wrist diameter, ankle diameter, forearm girth, thigh girth, chest depth, bicep girth, shoulder girth, elbow girth and the hip girth with an accuracy rate of 98.42%, by seven decision rule sets serving the purpose of dimension reduction.

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- Hina Tabassum, Muhammad Mutahir Iqbal, Zafar Mahmood, Maqsooda Parveen, and Irfan Ullah.
- Department of Statistics, Bahuddin Zakariya University, Multan, Pakistan.
- J Natl Med Assoc. 2023 Jun 1; 115 (3): 273282273-282.
AbstractThe decision tree used a generating set of rules based on various correlated variables for developing an algorithm from the target variable. Using the training dataset this paper used boosting tree algorithm for gender classification from twenty-five anthropometric measurements and extract twelve significant variables chest diameter, waist girth, biacromial, wrist diameter, ankle diameter, forearm girth, thigh girth, chest depth, bicep girth, shoulder girth, elbow girth and the hip girth with an accuracy rate of 98.42%, by seven decision rule sets serving the purpose of dimension reduction.Copyright © 2022 National Medical Association. Published by Elsevier Inc. All rights reserved.

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Gender classification from anthropometric measurement by boosting decision tree: A novel machine learning approach.

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