Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models - Zhao, Feng; Zhang, Hongzhen; Cheng, Danqing; Wang, Wenping; Li, Yongtian; Wang, Yisong; Lu, Dekun; Dong, Chunhui; Ren, Dingfei; Yang, Lixin (Frontiers in Medicine, 2022)
Background: Nodular thyroid disease is by far the most common thyroid disease and is closely associated with the development of thyroid cancer. Coal miners with chronic coal dust exposure are at higher risk of developing nodular thyroid disease. There are few studies that use machine learning models to predict the occurrence of nodular thyroid disease in coal miners. The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) models. Methods: This is a retrospective clinical study in which 1708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and their clinical physical examination data, including general information, laboratory tests and imaging findings, were collected. A synthetic minority oversampling technique (SMOTE) was used for sample balancing, and the data set was randomly split into a training and validation set in a ratio of 8:2. Lasso regression and correlation heat map were used to screen the predictors of the models, and five machine learning ( ML) models, including Extreme Gradient Augmentation (XGBoost), Logistic Classification (LR), Gaussian Parsimonious Bayesian Classification (GNB), Neural Network Classification (MLP) and Complementary Parsimonious Bayesian Classification (CNB) for their predictive efficacy, and the model with the highest AUC was selected as the optimal model for predicting the occurrence of nodular thyroid disease in coal miners. Result: Lasso regression analysis showed Age, H-DLC, HCT, MCH, PLT, and GGT as predictor variables for the ML models; in addition, heat maps showed no significant correlation between the six variables. In the prediction of nodular thyroid disease, the AUC results of the five ML models, XGBoost (0.892), LR (0.577), GNB (0.603), MLP (0.601) and CNB (0.543), with the XGBoost model having the largest AUC, the model can be applied in clinical practice. Conclusion: Although all five ML models were predictive of the risk of developing nodular thyroid disease in coal miners, the XGBoost model performed best and clinicians could adopt individualised preventive treatment strategies based on this model.