A comparative investigation of machine learning algorithms for predicting safety signs comprehension based on socio-demographic factors and cognitive sign features

Abstract

This study examines whether the socio-demographic factors and cognitive sign features can be used for envisaging safety signs comprehensibility using predictive machine learning (ML) techniques. This study will determine the role of different machine learning components such as feature selection and classification to determine suitable factors for safety construction signs comprehensibility. A total of 2310 participants were requested to guess the meaning of 20 construction safety signs (four items for each of the mandatory, prohibition, emergency, warning, and firefighting signs) using the open-ended method. Moreover, the participants were asked to rate the cognitive design features of each sign in terms of familiarity, concreteness, simplicity, meaningfulness, and semantic closeness on a 0–100 rating scale. Subsequently, all eight features (age, experience, education level, familiarity, concreteness, meaningfulness, semantic closeness, and simplicity) were used for classification. Furthermore, the 14 most popular supervised classifiers were implemented and evaluated for safety sign comprehensibility prediction using these eight features. Also, filter and wrapper methods were used as feature selection techniques. Results of feature selection techniques indicate that among the eight features considered in this study, familiarity, simplicity, and meaningfulness are found to be the most relevant and effective components in predicting the comprehensibility of selected safety signs. Further, when these three features are used for classification, the K-NN classifier achieves the highest classification accuracy of 94.369% followed by medium Gaussian SVM which achieves a classification accuracy of 76.075% under hold-out data division protocol. The machine learning (ML) technique was adopted as a promising approach to addressing the issue of comprehensibility, especially in terms of determining factors affecting the safety signs' comprehension. The cognitive sign features of familiarity, simplicity, and meaningfulness can provide useful information in terms of designing user-friendly safety signs.