Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study
Objectives Occupational noise-induced hearing loss (ONIHL) represents a prevalent occupational health condition, traditionally necessitating multiple pure-tone audiometry assessments. We have developed and validated a machine learning model leveraging routine haematological and biochemical parameter...
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BMJ Publishing Group
2025-04-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/4/e097249.full |
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| author | Wenting Feng Wen Zhang Yan Guo Naixing Zhang Liang Zhou Dafeng Lin Linlin Chen Caiping Li Liuwei Shi Xiangli Yang Peimao Li Dianpeng Wang |
| author_facet | Wenting Feng Wen Zhang Yan Guo Naixing Zhang Liang Zhou Dafeng Lin Linlin Chen Caiping Li Liuwei Shi Xiangli Yang Peimao Li Dianpeng Wang |
| author_sort | Wenting Feng |
| collection | DOAJ |
| description | Objectives Occupational noise-induced hearing loss (ONIHL) represents a prevalent occupational health condition, traditionally necessitating multiple pure-tone audiometry assessments. We have developed and validated a machine learning model leveraging routine haematological and biochemical parameters, thereby offering novel insights into the risk prediction of ONIHL.Design, setting and participants This study analysed data from 3297 noise-exposed workers in Shenzhen, including 160 ONIHL cases, with the data set divided into D1 (2868 samples, 107 ONIHL cases) and D2 (429 samples, 53 ONIHL cases). The inclusion criteria were formulated based on the GBZ49-2014 Diagnosis of Occupational Noise-Induced Hearing Loss. Model training was performed using D1, and model validation was conducted using D2. Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.Primary outcome measures Model creation data set and validation data sets: ONIHL.Results The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. On the test data set, the model achieved an AUC of 0.990. After implementing feature selection, the model was refined to include only 16 features, while maintaining strong performance on a newly acquired independent data set, with an AUC of 0.872, a balanced accuracy of 0.798, a sensitivity of 0.755 and a specificity of 0.840. The analysis of feature importance revealed that serum albumin (ALB), platelet distribution width (PDW), coefficient of variation in red cell distribution width (RDW-CV), serum creatinine (Scr) and lymphocyte percentage (LYMPHP) are critical factors for risk stratification in patients with ONIHL.Conclusion The analysis of feature importance identified ALB, PDW, RDW-CV, Scr and LYMPHP as pivotal factors for risk stratification in patients with ONIHL. The machine learning model, using XGBoost, effectively distinguishes patients with ONIHLamong individuals exposed to noise, thereby facilitating early diagnosis and intervention. |
| format | Article |
| id | doaj-art-dfae2c8b072e4155a2cf4c5d82655f1d |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-dfae2c8b072e4155a2cf4c5d82655f1d2025-08-20T02:27:06ZengBMJ Publishing GroupBMJ Open2044-60552025-04-0115410.1136/bmjopen-2024-097249Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling studyWenting Feng0Wen Zhang1Yan Guo2Naixing Zhang3Liang Zhou4Dafeng Lin5Linlin Chen6Caiping Li7Liuwei Shi8Xiangli Yang9Peimao Li10Dianpeng Wang11Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, ChinaDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Ministry of Education Key Laboratory, Beijing, ChinaChronic Noncommunicable Diseases Control and Prevention, Wuhan Center for Disease Control and Prevention, Wuhan, ChinaShenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, China1 Department of Social Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, Guangdong, ChinaPoison Detection Center, Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, ChinaSchool of Public Health, Southern Medical University, Guangzhou, Guangdong, ChinaSchool of Public Health, Southern Medical University, Guangzhou, Guangdong, ChinaShenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, ChinaShenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, ChinaShenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, ChinaSchool of Public Health, Southern Medical University, Guangzhou, Guangdong, ChinaObjectives Occupational noise-induced hearing loss (ONIHL) represents a prevalent occupational health condition, traditionally necessitating multiple pure-tone audiometry assessments. We have developed and validated a machine learning model leveraging routine haematological and biochemical parameters, thereby offering novel insights into the risk prediction of ONIHL.Design, setting and participants This study analysed data from 3297 noise-exposed workers in Shenzhen, including 160 ONIHL cases, with the data set divided into D1 (2868 samples, 107 ONIHL cases) and D2 (429 samples, 53 ONIHL cases). The inclusion criteria were formulated based on the GBZ49-2014 Diagnosis of Occupational Noise-Induced Hearing Loss. Model training was performed using D1, and model validation was conducted using D2. Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.Primary outcome measures Model creation data set and validation data sets: ONIHL.Results The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. On the test data set, the model achieved an AUC of 0.990. After implementing feature selection, the model was refined to include only 16 features, while maintaining strong performance on a newly acquired independent data set, with an AUC of 0.872, a balanced accuracy of 0.798, a sensitivity of 0.755 and a specificity of 0.840. The analysis of feature importance revealed that serum albumin (ALB), platelet distribution width (PDW), coefficient of variation in red cell distribution width (RDW-CV), serum creatinine (Scr) and lymphocyte percentage (LYMPHP) are critical factors for risk stratification in patients with ONIHL.Conclusion The analysis of feature importance identified ALB, PDW, RDW-CV, Scr and LYMPHP as pivotal factors for risk stratification in patients with ONIHL. The machine learning model, using XGBoost, effectively distinguishes patients with ONIHLamong individuals exposed to noise, thereby facilitating early diagnosis and intervention.https://bmjopen.bmj.com/content/15/4/e097249.full |
| spellingShingle | Wenting Feng Wen Zhang Yan Guo Naixing Zhang Liang Zhou Dafeng Lin Linlin Chen Caiping Li Liuwei Shi Xiangli Yang Peimao Li Dianpeng Wang Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study BMJ Open |
| title | Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study |
| title_full | Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study |
| title_fullStr | Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study |
| title_full_unstemmed | Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study |
| title_short | Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study |
| title_sort | construction of a risk prediction model for occupational noise induced hearing loss using routine blood and biochemical indicators in shenzhen china a predictive modelling study |
| url | https://bmjopen.bmj.com/content/15/4/e097249.full |
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