Safety risk prediction based on improved particle swarm optimization and least squares support vector machine

To address low accuracy, difficult parameter determination, and inefficient data use in safety risk prediction, this study proposes an intelligent prediction framework. It uses rough set theory to preprocess data (eliminate incompleteness, discretize continuous data, remove redundancy) and introduces an improved particle swarm optimization (IPSO) by introducing a mutation operator to optimize key parameters of the least squares support vector machine (LSSVM), forming the IPSO-LSSVM model. Experiments on 43 risk samples (33 for training, 10 for testing) show that it outperforms the BP neural network and standard PSO-LSSVM with faster convergence. It enriches integrated intelligent algorithms in safety risk prediction and provides technical references for improving prediction accuracy.