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

ABSTRACT: To compare the comprehensive performance of conventional logistic regression (LR) and seven machine learning (ML) algorithms in Noise-Induced Hearing Loss (NIHL) prediction, and to investigate the single nucleotide polymorphism (SNP) loci significantly associated with the occurrence and progression of NIHL. A total of 1,338 noise-exposed workers from 52 enterprises in Jiangsu Province were included in this study. 88 SNP loci involving multiple genes related to noise exposure and hearing loss were detected. LR and multiple ML algorithms were employed to establish the NIHL prediction model with accuracy, recall, precision, F-score, R² and AUC as performance indicators. Compared to conventional LR, the evaluated ML models Generalized Regression Neural Network (GRNN), Probabilistic

SUBMITTER: Lu J 

PROVIDER: S-EPMC12048554 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature