###### Abstract
Authors, such as [PERSON] (2018), agree that it is difficult to make decisions so that accidents do not happen again and that little is learned from accidents and incidents. Despite a large volume of data and information, occupational safety professionals can only sometimes detect patterns and report variables related to accidents in reports and other documents related to the safety area. A significant investment of time and money is required for these professionals to be able to find patterns that are repeated in workplace accidents. Authors such as [PERSON] et al. (2018) state that data from industrial reports are valuable for learning lessons from accidents and incidents that have already occurred. However, manual analysis of these documents requires many resources, making it preferable to search for computational solutions. Alternatively, using neural and non-neural methods can assist in analyzing databases recording accidents and their associated factors. Both types of methods are based on machine learning (ML), characterized by the use of mathematical models implemented in computational routines and capable of identifying patterns to be applied to new similar data in the future and decision-making ([PERSON] et al., 2017). Neural methods use the architecture of neurons, including enabling deep layers (deep learning), and promise some superiority in detecting patterns due to self-learning capacity and efficiency in determining optimal solutions ([PERSON] et al., 2017). Non-neural methods do not use the architecture of neurons to detect patterns. Still, they can present better solutions than neural methods for several problems ([PERSON] et al., 2017), with the advantage of being simple and requiring less computational effort and training time ([PERSON] et al., 2017). Given the above, this article aims to verify the performance of neural and non-neural methods used to classify the severity of work accidents in the footwear industry complex. Based on the accuracy, precision, recall, and F-Score values, it is expected to define which method performs best in identifying patterns and classifying the severity of work accidents in the footwear industry. In addition to the novelty of comparing several ML methods to analyze accidents in the footwear industry, this study brings relevant contributions by showing the excellent performance of methods such as 1D convolutional neural networks (1D-CNN) and bidirectional long-short-term memory (Bi-LSTM) to classify occupational accidents, something little explored by previous studies. The findings of this research suggest that neural and non-neural methods performed well in classifying accidents at work in the footwear industry. Methods such as 1D-CNN and Bi-LSTM performed better but required a long training time. Methods such as random forest (RF) and extreme gradient boosting (XGBoost) presented relatively lower performance. However, they require less training time and are still viable alternatives for occupational safety managers in the footwear industry. This article is organized as follows: Section 1 presents the introduction of the article, highlighting the topic, relevance, and objectives of this article. Section 2 presents a brief review of the related work. Section 3 indicates the methodology used, highlighting the study design, data collection, study variables, classification methods (non-neural and neural), classification model performance, and the importance of factors in classification. Section 4 presents the results, highlighting the characterization and distribution of accidents, the result of model comparison, and the importance of different factors. Finally, Sections 5 and 6 discuss the research's main findings, including the study's implications and limitations and the conclusion of this article, respectively.