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To construct a prediction model for metabolic syndrome (Metabolic Syndrome, MetS) in railway employees based on machine learning algorithms (Machine Learning, ML) and evaluate the prediction performance.
The time to the onset of metabolic syndrome was used as the outcome variable, with demographic characteristics and biochemical indicators as predictive variables. Univariate analysis was conducted to select predictive indicators. The study subjects were randomly divided into a training set and a test set in a 7:3 ratio. Cox proportional hazards regression, Random Forest (Random Survival Forest, RSF), and Gradient Boosting Machine (Gradient Boosting Machine, GBM) were used to build metabolic syndrome prediction models. Model performance was assessed using the area under the receiver operating characteristic curve (Area under curve, AUC), concordance index (C-index), sensitivity, specificity, accuracy, and F1 score. A risk calculator was created using the shiny package.
This study included 17 087 subjects and collected 28 indicators. Univariate analysis identified 22 statistically significant indicators. In the training set, the areas under the curve (area under the curve, AUC) of the prediction models constructed by Cox, RSF, and GBM were 0.870,0.938, and 0.891, respectively; C-index values were 0.853,0.935, and 0.843; sensitivity was 0.612,0.968, and 0.628; specificity was 0.933,0.742, and 0.994; accuracy was 0.678,0.788, and 0.703; F1 scores were 0.751,0.839, and 0.749.
The RSF model outperformed the Cox model and the GBM model in predicting metabolic syndrome among railway employees, providing a scientific basis for early identification of metabolic syndrome and aiding in the implementation of primary prevention measures.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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