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 pro...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00050-1 |
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| author | Jie Lu Xinhao Lu Yixiao Wang Hengdong Zhang Lei Han Baoli Zhu Boshen Wang |
| author_facet | Jie Lu Xinhao Lu Yixiao Wang Hengdong Zhang Lei Han Baoli Zhu Boshen Wang |
| author_sort | Jie Lu |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-51f23db9f3884f1fbdfb56f5d59eceb7 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-51f23db9f3884f1fbdfb56f5d59eceb72025-08-20T01:47:29ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-00050-1Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP lociJie Lu0Xinhao Lu1Yixiao Wang2Hengdong Zhang3Lei Han4Baoli Zhu5Boshen Wang6Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast UniversitySchool of Cyber Science and Engineering, Southeast UniversitySchool of Public Health, Nanjing Medical UniversityInstitute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Academy of Preventive Medicine)Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Academy of Preventive Medicine)Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast UniversityKey Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast UniversityAbstract 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 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.https://doi.org/10.1038/s41598-025-00050-1NIHLMachine learningLogistic regressionSNP loci |
| spellingShingle | Jie Lu Xinhao Lu Yixiao Wang Hengdong Zhang Lei Han Baoli Zhu Boshen Wang Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci Scientific Reports NIHL Machine learning Logistic regression SNP loci |
| title | Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci |
| title_full | Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci |
| title_fullStr | Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci |
| title_full_unstemmed | Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci |
| title_short | Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci |
| title_sort | comparison between logistic regression and machine learning algorithms on prediction of noise induced hearing loss and investigation of snp loci |
| topic | NIHL Machine learning Logistic regression SNP loci |
| url | https://doi.org/10.1038/s41598-025-00050-1 |
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