Mathematical modelling for the evaluation of body fat percentage by data of body components

Name: Rungsiman Ngekkoy

Organization : Suranaree University of Technology. School of Biomedical Innovation Engineering

Email : lowlow0103@gmail.com

Name: Natakon Nawaratana

Organization : Suranaree University of Technology. School of Mathematics

Email : natakonnawaratana@gmail.com

Name: Jirakit Boonmunewai

Organization : Suranaree University of Technology. School of Mathematics

Email : toontoon18@gmail.com

Name: Jessada Tanthanuch

Organization : Suranaree University of Technology. School of Mathematics

Email : jessada@g.sut.ac.th

keyword: RapidMiner Studio

ThaSH: Mathematical models

; Body Fat Percentage

ThaSH: Linear models (Statistics)

ThaSH: Parameter estimation

Abstract: This research aims to create a mathematical model for evaluating the percentage of body fat. The model was done by RapidMiner Studio version 9.10 by using the open-source dataset obtained from https://www.kaggle.com/fedesoriano/body-fat-prediction-dataset. The data is composed of 15 features, consisting of the percentage of body fat, density (determined from underwater weighing), age, weight, height, the circumference of neck, chest, abdomen, hip, thigh, knee, ankle, biceps (extended), forearm, and wrist. The research procedure is divided into 2 main steps: feature engineering and modeling. First, the 14 features of data are analyzed to model the percentage of body fat by applying featuring engineering and four modeling techniques: generalized linear model (GLM), gradient boosted tree (GBT), deep learning (DL), and support vector machine (SVM). The obtained features are then used for the creation of the forecasting models. It is found that no matter what technique is used for feature engineering, the forecasting model using the GLM technique provides the best performance.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2022

Modified: 2025-09-15

Issued: 2025-09-15

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BibliograpyCitation : In King Mongkut's University of Technology North Bangkok Faculty of Applied Science, Thai Statistical Association (TSA) and Statistics Cooperative Research Network (Statistics CRN). The Proceeding of International Conference on Applied Statistics (ICAS 2022) (pp.115-120). Bangkok : King Mongkut's University of Technology North Bangkok

eng

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