Predictive Modeling of Lung Cancer Risk in Workers Using Dropouts Meet Multiple Additive Regression Trees

Lung cancer remains a leading cause of cancer mortality, and preventable occupational and environmental exposures may compound risk in working-age populations. This study developed and compared predictive models for lung cancer risk using a publicly available tabular dataset (Kaggle; n = 1,000) containing demographic, lifestyle, symptom, and exposure-related variables. After standard preprocessing and an 80/20 train-test split, a Classification and Regression Tree (CART), a dropout-regularized gradient-boosted tree model (DART), k-nearest neighbors (KNN), and Gaussian Naïve Bayes were trained and evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). CART achieved the highest accuracy (84.5%), while KNN achieved the highest precision (78.7%). DART produced the best F1-score (77.3%) and the highest AUC (0.801), suggesting a favorable balance between sensitivity and specificity when accounting for class imbalance. Feature-importance patterns in the final DART model highlighted occupational hazards, smoking habits, genetic predisposition, and air pollution exposure as leading contributors to model-based risk stratification in occupational settings. These findings suggest that regularized ensemble tree methods can support stable risk stratification and may complement screening by prioritizing individuals who warrant closer evaluation. The analysis is limited by the modest sample size and reliance on a single public dataset; external validation in occupational cohorts with measured exposure histories is required before practical implementation.

@article{Byeon2026,
  title     = {Predictive Modeling of Lung Cancer Risk in Workers Using Dropouts Meet Multiple Additive Regression Trees},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Haewon Byeon},
  doi       = {10.14569/IJACSA.2026.0170348},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170348}
}