Title: Knowledge discovery for anthropometric measures using data mining techniques

Authors: Ali Chegini; Alireza Dehghan; Rouzbeh Ghousi

Addresses: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran ' Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran ' Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, 16844, Iran

Abstract: The article presents a novel application for data mining techniques on anthropometric measures to uncover hidden relationships and associations between different body measurements. The anthropometric data consists of 111 samples with 15 features including basic demographics, body mass index (BMI), and ten anthropometric measures. The research utilises the CRISP-DM methodology to form an applicable data analytics framework for anthropometric measurements. Various data mining methods were applied including regression analysis to predict stature, clustering algorithms (K-means and hierarchical clustering) to segment the data, classification techniques (SVM and decision trees) to categorise BMI status, and association rules mining to uncover patterns between body dimensions and BMI category. The results demonstrated strong correlations between anthropometric dimensions with stature and weight; three distinct physical trait profiles clusters emerging from the K-means algorithm. The findings can facilitate ergonomic design and promote health assessments, and personalised interventions.

Keywords: ergonomics; human factors; anthropometry; CRISP-DM; machine learning.

DOI: 10.1504/IJDMMM.2025.150988

International Journal of Data Mining, Modelling and Management, 2025 Vol.17 No.4, pp.406 - 432

Received: 24 Sep 2023
Accepted: 11 Sep 2024
Published online: 07 Jan 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article