## 1 Introduction  
Recently, 3D body scan technology has fundamentally changed the way of measuring the human body. In the fields of anthropometry, ergonomics, product design and apparel design, taking accurate body measurements is a critical step in designing great products that fit the human body. A systematic measurement protocol ensures that the product fits comfortably, moves with the body, and is aesthetically appealing [1]. However, datasets extracted from the 3D body scanner often suffer from missing values through the automatic measurement extraction process or occlusion of specific body parts during the scan, which can result in incomplete and unusable datasets [2]. As a result, tedious extra efforts would be required to manually fill the missing data points in the database. However, a natural question to ask is whether or not it is possible to accurately predict the missing body measurements by exploring data-driven approaches without knowing the explicit correlations among the measurements. If this is possible, the prediction can be much more accurate such that the efficiency of working with 3D body measurement datasets can be significantly improved. Inspired by successful applications of machine learning (ML) methods in medical and biological domain [3, 4, 5, 6], we look into this very practical yet important question in this paper. In past decades, ML techniques have been utilized to solve the classification and regression problems [7], leading to many noteworthy applications such as wind speed prediction [8], temperature forecasting [9], solar irradiance forecasting [10], train arriving time prediction [11], construction strength prediction [12], speech recognition [13],image recognition [14], etc. ML techniques have been used in human body-related areas [15; 16; 17]. [PERSON] and [PERSON] [15] proposed a method combined with a statistical shape model of easy-to-obtain measurements to create 3D human shapes. [PERSON] et al. [16] proposed a self-supervised method to estimate a 3D body pose from multi-view images. [PERSON] et al. [17] reviewed the recent work in point cloud-based pose estimation of the human body in various applications such as human-computer interaction, video analysis, and autonomous driving. ML techniques have also demonstrated good performance in 3D Body Measurement (BM) as an accurate estimator and the closest works to this paper are in [18; 19; 20]. [PERSON] and [PERSON] [18] developed a parametric human body shape modeling system and generated body shape models based on the input body sizes via radial basis function. [PERSON] et al. [19] proposed a novel prediction model to estimate the body fat percentage by analyzing 3D body shapes following the concept of 'visual cue' by analyzing the second-order shape descriptors and establishing the baseline regression model for feature selection of the zeroth-order shape descriptors with improved baseline prediction. [PERSON] et al. [20] proposed a backpropagation artificial neural network to predict pattern-making-related body dimensions by inputting a few key human body dimensions via the database of anthropometric measurements of 120 young females from the northeastern region of China. However, the above references do not directly address the data missing issue from the 3D body scanner. Furthermore, none of these works consider the combination of feature selection and Bayesian search, which could greatly improve the training efficiency and prediction accuracy. Feature selection has been widely used in ML techniques to map redundant, relevant and irrelevant features into a smaller set of features than the original dataset [21; 22; 23]. [PERSON] et al. [21] investigated the effectiveness of feature extraction for body fat prediction by considering the characteristics of different features (e.g., body measurements). It evaluated the performance of feature extraction approaches by comparing well-known prediction models including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression and Artificial Neural Network.