Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci

and AUC as performance indicators. Compared to conventional LR, the evaluated ML models Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), Genetic Algorithm-Random Forests (GA-RF) demonstrate superior performance and were considered to be the optimal models for processing large-scale SNP loci dataset. The SNP loci screened by these models are pivotal in the process of NIHL prediction, which further improves the prediction accuracy of the model. These findings open new possibilities for accurate prediction of NIHL based on SNP locus screening in the future, and provide a more scientific basis for decision-making in occupational health management.