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.

Nutritional status and growth centiles using anthropometric measures of school-aged children and adolescents from Multan district.

Background: Nutritional status among children and adolescents is assessed using growth rates. The aim of this study was to assess age- and gender-specific height, weight, and body mass index (BMI) centiles among children and adolescents relative to World Health Organization (WHO) references.

Methods: A sample of 1040 school-aged children and adolescents aged 3-18 years from Multan District in Pakistan were selected for the study between January and March 2020.