Prediction of sunburn in power grid workers using machine learning models

Power grid workers may suffer sunburn from exposure to excessive ultraviolet radiation while working outdoors. There are many factors that can affect sunburn, including meteorological factor, individual factor, physical work factor and other potential factors. Traditional prediction methods only consider single variable and lack of in-depth mining for complex variables. Machine learning algorithms can find the key factors affecting sunburn and calculate the optimal prediction model through multiple iterations within the algorithm, which is increasingly used in the prediction of various occupational diseases. In this study, a variety of machine learning models including random forest, support vector machine, logistic regression and XGboost were used to predict the risk of sunburn of power grid workers. The accuracies of 10-fold cross-validation were 80.7%, 81.1%, 80.6% and 79.6%. Logistic regression model had the best performance.