Artificial neural network modelling using complete blood count for the early detection of black lung disease among Indonesian coal miners: A retrospective longitudinal study
Blood cell counts alone can flag early lung disease in coal miners, offering a simple screening tool to protect workers before symptoms develop.
A retrospective longitudinal study developed and internally validated an ANN using routine CBC parameters to predict early pneumoconiosis in 807 Indonesian coal miners over 8 years. Eosinophil and monocyte counts were the most influential predictors, supporting integration of CBC-based ML screening into occupational health surveillance for high-risk populations.
What the study was
- Study design
- Retrospective longitudinal ML study with ANN (n=807 coal miners, 2013-2021)
- Population
- 807 Indonesian coal miners, 8-year longitudinal follow-up
- Sample size
- 807
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- J Infect Public Health
Why it surfaced
CBC-based ANN for occupational lung disease early detection; n=807 longitudinal dataset; demonstrates practical utility of routine hematology data for ML-driven disease prediction in underserved occupational population.
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