Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study

Figures & data

Figure 1 Schematic diagram of neural network training.

Figure 2 Support vector machine diagram.

Figure 3 Local schematic diagram of the two-level regression tree model.

Table 1 The Sensitivity, Specificity, and AUC of Three Machine Learning Methods (Neural Network, Support Vector Machine, and Decision Tree) for Evaluating the Depression Status of Chinese Recruits

	Neural Network	Support Vector Machine	Decision Tree
Sensitivity	0.931	0.934	0.941
Specificity	0.600	0.588	0.433
AUC	0.860	0.862	0.734

Abbreviation: AUC, area under the curve.

Figure 4 Receiver operating characteristics (ROC) curve for depression in Chinese recruits from three machine learning methods: support vector machine (blue line), neural network (orange line), and decision tree (green line).

Table 2 Comparison of Regression Model Parameters of Three Machine Learning Methods (Neural Network, Support Vector Machine, and Decision Tree)

	Neural Network	Support Vector Machine	Decision Tree
R^2	0.465	0.544	0.477
MSE	64.884	60.087	68.043
RMSE	8.055	7.752	8.249
MAE	4.821	4.659	5.243
MAPE	0.897	1.030	0.822

Abbreviations: R², coefficient of determination; MSE, mean square error; RMSE, root mean square error; MAE, mean absolute error; MAPE, mean absolute percentage error.