Article
Hybrid Machine Learning Model for Body Fat Percentage
Prediction Based on Support Vector Regression and Emotional
Artificial Neural Networks
Solaf A. Hussain
1,2,
*, Nadire Cavus
2,3
and Boran Sekeroglu
4
Citation: Hussain, S.A.; Cavus, N.;
Sekeroglu, B. Hybrid Machine
Learning Model for Body Fat
Percentage Prediction Based on
Support Vector Regression and
Emotional Artificial Neural Networks.
Appl. Sci. 2021, 11, 9797. https://
doi.org/10.3390/app11219797
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 16 September 2021
Accepted: 14 October 2021
Published: 20 October 2021
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1
Computer Science Department, College of Science, University of Sulaimani, Sulaimani 334, Iraq
2
Computer Information Systems, Near East University, 99138 Nicosia, Cyprus; nadire.cavus@neu.edu.tr
3
Computer Information Systems Research and Technology Centre, Near East University,
99138 Nicosia, Cyprus
4
Computer Engineering, Near East University, 99138 Nicosia, Cyprus; boran.sekeroglu@neu.edu.tr
* Correspondence: solaf.hussein@univsul.edu.iq or 20176641@neu.edu.tr
Abstract:
Obesity or excessive body fat causes multiple health problems and diseases. However,
obesity treatment and control need an accurate determination of body fat percentage (BFP). The
existing methods for BFP estimation require several procedures, which reduces their cost-effectivity
and generalization. Therefore, developing cost-effective models for BFP estimation is vital for
obesity treatment. Machine learning models, particularly hybrid models, have a strong ability to
analyze challenging data and perform predictions by combining different characteristics of the
models. This study proposed a hybrid machine learning model based on support vector regression
and emotional artificial neural networks (SVR-EANNs) for accurate recent BFP prediction using a
primary BFP dataset. SVR was applied as a consistent attribute selection model on seven properties
and measurements, using the left-out sensitivity analysis, and the regression ability of the EANN
was considered in the prediction phase. The proposed model was compared to seven benchmark
machine learning models. The obtained results show that the proposed hybrid model (SVR-EANN)
outperformed other machine learning models by achieving superior results in the three considered
evaluation metrics. Furthermore, the proposed model suggested that abdominal circumference is a
significant factor in BFP prediction, while age has a minor effect.
Keywords:
support vector regression; emotional artificial neural network; body fat percentage;
hybrid model
1. Introduction
Obesity is a public health problem worldwide [
1
]. Researchers predict that obesity
causes several major health issues, such as mood disorders, cardiovascular diseases, respi-
ratory ailments, and digestive issues [
2
]. In the medical, healthcare, and fitness sectors, a
person is determined as obese by calculating the person’s body mass index (BMI), which
considers the person’s body weight divided by body height square [
3
]. BMI is a beneficial
measurement, particularly for population-based screening. However, subgroups of obese
individuals with normal metabolic health but a higher body mass index or obese individu-
als with poor metabolic health but an average body mass index exist [
4
]. Therefore, BMI
might not capture people at a higher risk of cardio-metabolic disorders, such as type 2
diabetes and cardiovascular disease.
The lack of effective information from BMI changed researchers’ focus to body fat
percentage (BFP), which measures fat in the body and provides a more accurate assessment.
However, to criticize the amount of obesity and prevent it, it is critical to precisely assess
BFP. Several methods can accomplish the estimation of the BFP. Some approaches, such
as anthropometry models, consider the age, weight, waist circumference, and skinfold
Appl. Sci. 2021, 11, 9797. https://doi.org/10.3390/app11219797 https://www.mdpi.com/journal/applsci
