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

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.

Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness

“…As seen in Figure 3, the study EANN model has three hormones' secretors with ith nerve-cell output. The ith nerve-cell output was explained using Equation (1) as per [36].…”

Predicting Body Fat Percentage: A Machine Learning Approach

“…In this exploration, we draw upon a range of scholarly works that have ventured into the domain of body fat prediction through machine learning. These studies include Fan et al (2022) and Hussain et al (2021), who have explored feature extraction and hybrid machine learning models, respectively, for body fat percentage prediction. Fujihara et al (2023) and Xu et al (2023) have investigated the prediction of body weight and body fat percentage using machine learning techniques.…”

Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling

“…In recent years, the increasing availability of large MRI datasets from national biobanks has enabled in-depth research towards understanding the impact of diet, lifestyle and genetics on the accumulation and distribution of liver fat [31]. Advancements in artificial intelligence applied to biomedical research have driven the development of regression models that predict body fat using multivariate supervised machine learning methods, such as support vector regression, gradient boosting regression and random forests, all of which are dependent on large datasets (involving more than 100 subjects) [45][46][47][48][49][50].…”