###### Abstract
Even though the proposed model had high prediction ability for BFP prediction and considered a large number of features, it still lacked interpretation of the significant features that affect BFP prediction. All these studies showed the ability of machine learning models, particularly the ANN and SVR, to predict BFP using minimized data that do not require the utilization of some outer information-handling units. However, the variety of machine learning (ML) models and the proposal of different types of neural networks led to the implementation of different ensemble or hybrid models in order to increase the prediction ability for BFP. Generally, hybrid machine learning approaches that utilized forecasting models that incorporated two modeling algorithms were offered [10]. The initial algorithm of hybrid models recognizes and determines the variables that play a significant role in explaining the model's outcomes but whose number is limited. The following algorithms of the hybrid model use explanatory variables to create predictions. Therefore, improved prediction rates are achieved using the abilities of different algorithms. [PERSON] [11] and [PERSON] [12] constructed hybrid models based on a multilayer feed-forward neural network (MLFFNN), SVM, decision tree (DT), and linear regression (LR) to optimize feature selection models. These studies used a body fat dataset developed by [PERSON] [10], including 14 attributes of 252 males. It was concluded that an anthropometric measurement could effectively be considered for predicting BFP; however, the investigation of gender effect was ignored because of not including female data. The employment of feature selection models to criticize significant features is crucial for the performance enhancement for BFP prediction. Therefore, studies [11; 12] proposed hybrid models for feature selection and multiple ML prediction algorithms with high prediction rates. However, these models used relatively small datasets with a restricted number of male samples, and their proposed models were never tested with relatively large datasets. The improvements of the ANN to advance the performance [13] or to consider human emotions to improve the learning abilities of neural networks were investigated in different studies [14; 15], and recently, emotional artificial neural networks (EANNs) were proposed [16]. In EANNs, the \"emotion\" ties to the activity of neurophysiological reaction in biological terms: humans might be capable of changing their response to situations using their attitudes and emotions. Thus, an input circle is established by combining the neuronal and hormonal frameworks. This improves the model's learning capacity, reduces computational costs, and provides effective convergence with limited data [16]. The robustness of the obtained results led the EANN to be implemented in various fields, such as forecasting the steady cross-sectional math of alluvial channel profiles [17], forecasting suspended silt loads [18], and the prediction of the strength of concrete under compression [19]. Based on the above-discussed information, ML-based BFP prediction requires hybrid studies with comprehensive and primary datasets and features besides the performance of the algorithm. In this study, we proposed a hybrid ML model (SVR-EANN) by combining support vector regression and emotional artificial neural networks. The model includes a feature selection procedure using SVR and uses the prediction ability of EANN, which combines the basic structure of the neural network with various emotional functions, to achieve high prediction rates and determine the most significant factors that affect BFP. In addition, a primary dataset, including age, gender, height, weight, abdominal circumference (abdominal C), waist-to-hip ratio (WHR), which implies the ratio between waist circumference and hip circumference, BMI, and BFP, was considered.