Table 1 Preliminary results
From: Single classifier vs. ensemble machine learning approaches for mental health prediction
Machine learning | Accuracy (%) | Precision (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|
Logistic regression | 79.63 | 76.19 | 85.56 | 73.82 |
Gradient boosting | 81.22 | 76.13 | 90.37 | 72.25 |
Neural networks | 78.57 | 73.45 | 88.77 | 68.59 |
K-nearest neighbours | 81.22 | 78.43 | 85.56 | 76.96 |
Support vector machine | 80.69 | 74.15 | 93.58 | 68.06 |
Deep neural networks | 79.89 | 73.62 | 92.51 | 67.54 |
Ensemble approach | ||||
 Voting classifier | 81.75 | 75.87 | 92.51 | 71.20 |
 Extreme gradient boosting | 80.69 | 75.22 | 90.91 | 70.68 |