ArticleScientific reports2025
Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Development and Validation of an Interpretable Machine Learning Model Based on Routine Blood Biomarkers: For Predicting Age-Related Hearing Loss.Diagnostics (Basel, Switzerland) · 2026Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- Application value of multiphase contrast-enhanced computed tomography radiomics in preoperative evaluation of peritoneal metastasis in gastric cancer.World journal of gastrointestinal oncology · 2026Article
- Unveiling the non-linear synergistic effects of smoking and aging on cataract-AMD comorbidity: an explainable artificial intelligence approach.Frontiers in public health · 2026Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
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Authors and funding
7 authors.
Funding
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, R
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