Research Article
A Comparative Study on the Prediction of Occupational Diseases in China with Hybrid Algorithm Combing Models
Table 1
The models, programming languages, libraries, and parameter adjustments used in this study.
| | Models | Programming languages | Libraries | Parameters |
| | GM | R (version 3.6.1) | Self-compiled function | EGM | | ODGM | | EDGM | | DGM | | Verhulst |
| | KNN | R (version 3.6.1) | kknn (version 1.3.1) | k = 2 | | caret (version 6.0–81) | train.kknn() | | kernel = inv |
| | SVM | R (version 3.6.1) | e1071 (version 1.8–8) | Kernel |
| | RF | R (version 3.6.1) | RandomForest (version 4.6–1.4) | mtry = 1 | | ntree |
| | GBM | R (version 3.6.1) | xgboost (version 0.82.1) | nrounds | | colsample_bytree | | min_child_weight | | Eta | | Gamma | | Subsample | | max_depth |
| | ANN | R (version 3.6.1) | nnet (version 7.3–12) | Size | | Decay |
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