Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

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....

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Autores principales:	Solaf A. Hussain, Nadire Cavus, Boran Sekeroglu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	support vector regression emotional artificial neural network body fat percentage hybrid model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/91519aa093774c39a4bc4f8d7f3234a8
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id	oai:doaj.org-article:91519aa093774c39a4bc4f8d7f3234a8
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spelling	oai:doaj.org-article:91519aa093774c39a4bc4f8d7f3234a82021-11-11T14:57:10ZHybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks10.3390/app112197972076-3417https://doaj.org/article/91519aa093774c39a4bc4f8d7f3234a82021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9797https://doaj.org/toc/2076-3417Obesity 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.Solaf A. HussainNadire CavusBoran SekerogluMDPI AGarticlesupport vector regressionemotional artificial neural networkbody fat percentagehybrid modelTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9797, p 9797 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	support vector regression emotional artificial neural network body fat percentage hybrid model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	support vector regression emotional artificial neural network body fat percentage hybrid model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Solaf A. Hussain Nadire Cavus Boran Sekeroglu Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
description	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.
format	article
author	Solaf A. Hussain Nadire Cavus Boran Sekeroglu
author_facet	Solaf A. Hussain Nadire Cavus Boran Sekeroglu
author_sort	Solaf A. Hussain
title	Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
title_short	Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
title_full	Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
title_fullStr	Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
title_full_unstemmed	Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks
title_sort	hybrid machine learning model for body fat percentage prediction based on support vector regression and emotional artificial neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/91519aa093774c39a4bc4f8d7f3234a8
work_keys_str_mv	AT solafahussain hybridmachinelearningmodelforbodyfatpercentagepredictionbasedonsupportvectorregressionandemotionalartificialneuralnetworks AT nadirecavus hybridmachinelearningmodelforbodyfatpercentagepredictionbasedonsupportvectorregressionandemotionalartificialneuralnetworks AT boransekeroglu hybridmachinelearningmodelforbodyfatpercentagepredictionbasedonsupportvectorregressionandemotionalartificialneuralnetworks
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Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

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