###### 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. supportun vector regression; emotional artificial neural network; body fat percentage; hybrid model +
Footnote †: journal: Medical  
thickness of a person to make assumptions based on physics laws and mathematical computations [5]. Other clinical approaches use special instruments to measure body composition. However, models such as underwater weighing (UWW), dual-energy X-ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA), computed tomography (CT), magnetic resonance imaging (MRI), and near-infrared interactome (NIR) are more accurate and more expensive. Researchers who have studied the BIA instrument relied heavily on body composition measurement and estimation of BFP throughout the last few years [6]. However, the above-mentioned methods could produce ineffective estimation either in terms of cost or inaccuracy. Therefore, researchers have begun to use the capabilities of artificial intelligence (AI) and machine learning (ML) models to solve a complex non-linear problem as a cost-effective and more accurate alternative for BFP prediction. This led to the proposal of high-performance prediction models for BFP from simple anthropometric measurements and basic information such as gender and age using several machine learning models. The most commonly and frequently used models for BFP prediction are artificial neural networks (ANNs) and support vector regression (SVR), which can create non-linear relations between the limited number of instances [7]. [PERSON] [1] employed an ANN to predict the BFP, and they considered the age, gender, and BMI of the persons as instances for the prediction of BFP in their study. In addition, [PERSON] [8] explored how relevant features are in predicting BFP in the presence of 39 anthropometric and laboratory measurements using ANNs and SVR. It was concluded that waist circumference is the most associated trait with a successful prediction of BFP. However, the uninterpretable data processing and output prediction of ANNs prevented the determination of the importance and the impact of relative input features in BFP prediction, even though considerable prediction results were achieved. Furthermore, [PERSON] et al. [9] presented an improved relative error support vector machine to predict BFP. Their study considered two datasets: a body fat dataset [10] and 39 anthropometric and laboratory measurements of 852 observations. The proposed model included a bias error control term, and their feature selection model was based on removing irrelevant features.