Optimizing a Classification Model to Evaluate Individual Susceptibility in Noise-Induced Hearing Loss: Cross-Sectional Study
Bibliographic record
Abstract
Background: Noise-induced hearing loss (NIHL), one of the leading causes of hearing loss in young adults, is a major health care problem that has negative social and economic consequences. It is commonly recognized that individual susceptibility largely varies among individuals who are exposed to similar noise. An objective method is, therefore, needed to identify those who are extremely sensitive to noise-exposed jobs to prevent them from developing severe NIHL. Objective: This study aims to determine an optimal model for detecting individuals susceptible or resistant to NIHL and further explore phenotypic traits uniquely associated with their susceptibility profiles. Methods: Cross-sectional data on hearing loss caused by occupational noise were collected from 2015 to 2021 at shipyards in Shanghai, China. Six methods were summarized from the literature review and applied to evaluate their classification performance for susceptibility and resistance of participants to NIHL. A machine learning (ML)-based diagnostic model using frequencies from 0.25 to 12 kHz was developed to determine the most reliable frequencies, considering accuracy and area under the curve. An optimal method with the most reliable frequencies was then constructed to detect individuals who were susceptible versus resistant to NIHL. Phenotypic characteristics such as age, exposure time, cumulative noise exposure, and hearing thresholds (HTs) were explored to identify these groups. Results: A total of 6276 participants (median age 41, IQR 33-47 years; n=5372, 85.6% men) were included in the analysis. The ML-based NIHL diagnostic model with misclassified subjects showed the best performance for identifying workers in the NIHL-susceptible group (NIHL-SG) and NIHL-resistant group (NIHL-RG). The mean HTs at 4 and 12.5 kHz showed the highest predictive value for detecting those in the NIHL-SG and NIHL-RG (accuracy=0.78 and area under the curve=0.81). Individuals in the NIHL-SG selected by the optimized model were younger than those in the NIHL-RG (median 28, IQR 25-31 years vs median 35, IQR 32-39 years; P<.001), with a shorter duration of noise exposure (median 5, IQR 2-8 years vs median 8, IQR 4-12 years; P<.001) and lower cumulative noise exposure (median 90, IQR 86-92 dBA-years vs median 92.2, IQR 89.2-94.7 dBA-years; P<.001) but greater HTs (4 and 12.5 kHz; median 58.8, IQR 53.8-63.8 dB HL vs median 8.8, IQR 7.5-11.3 dB HL; P<.001). Conclusions: An ML-based NIHL diagnostic model with misclassified subjects using the mean HTs of 4 and 12.5 kHz was the most reliable method for identifying individuals susceptible or resistant to NIHL. However, further studies are needed to determine the genetic factors that govern NIHL susceptibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".