## 1 Introduction  
In recent decades, various industrial safety management systems have been introduced and improved upon; however, occupational safety remains unstable and low. In particular, in the construction industry, various fields and types of work are undertaken simultaneously and organically, and a wide range of hazard factors are present. Thus, safety management in the construction industry is difficult, owing to the complexity of numerous activities and the involvement of various entities [1]. Moreover, most of the work is performed by humans; hence, techniques to predict occupational accidents through simple correlations--and thereby establish safety measures to prevent them--are limited. Thus, extensive research has been conducted over the past decades to increase the safety performance of construction sites. In 2002, [PERSON] conducted a study to improve the safety performance of and incentives for minimizing injuries on construction sites [2]; then, in 2005, [PERSON] et al. analyzed the correlations of factors contributing to different types of falls [3]. In 2008, [PERSON] et al. analyzed fundamental safety factors through a questionnaire, based on the practical abilities of construction safety experts to ensure site safety, and they proposed measures to improve safety in the industry [4]. However, the existing literature on safety improvement methods is limited in certain respects. For example, the concept of occupational safety at construction sites was not well defined at an early stage; thus, the data collected were not sufficient to identify accidents. Furthermore, the dynamic characteristics of construction projects have not been adequately reflected in most studies, and because most of the developed models did not rely on empirical data, they could only be applied to limited cases. After that, to overcome the problem of insufficient data, numerous studies have been conducted using the previous occupational accident data, including more detailed accident information. For example, in 2009, [PERSON] et al. argued that injury data-based industrial accident analysis has focused on identifying common causes of occupational accidents to design appropriate preventive measures [5]. In 2011, [PERSON] et al. applied a dataset complexity reduction method to simplify the process of understanding the occupational accident data [6]. In 2014, [PERSON] et al. applied various technologies such as machine learning (ML) and big data analysis (including data statistics and data mining) to analyze occupational accident data [7]. In 2019, [PERSON] et al. considered the importance of standardizing accident data prior to analyzing construction accident data [8]. As described above, the analysis of occupational accidents in the past was mainly performed through descriptive statistical methods [9], but recently, it has been developed into accident prediction studies applying various ML analysis methods to accident data. Even with traditional statistical methods, advanced analysis can be performed using large datasets, to identify hidden patterns in the data [10]. However, many studies have shown that when comparing traditional statistical models with ML methods, the latter is superior in predicting future events [8]. Thus, disaster and occupational accident analyses have been performed across various fields using ML methods. Typical ML-based classification algorithms include decision trees (DTs), artificial neural networks (ANNs), extreme learning machines, Bayesian networks, and support vector machines (SVMs) [11]. More specifically, ANNs [12; 13; 14], SVMs [15; 16], and DTs [17; 18; 19; 20] suitable for prediction rather than other ML methods have been used as the main prediction methods of occupational accident. ANNs generally provide better results than conventional, simple classification techniques. However, because this approach is a black-box technology, it is difficult for humans to interpret, hence it is difficult to identify the correlations between variables in the accident data [21].