Table 2 Final results
From: Single classifier vs. ensemble machine learning approaches for mental health prediction
Machine learning | Accuracy (%) | Precision (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|
Logistic regression | 84.00 | 82.86 | 87.88 | 79.66 |
Gradient boosting | 88.80 | 84.21 | 96.97 | 79.66 |
Neural networks | 88.00 | 84.00 | 95.45 | 79.66 |
K-nearest neighbours | 84.00 | 84.85 | 84.85 | 83.05 |
Support vector machine | 82.40 | 84.38 | 81.82 | 83.05 |
Deep neural networks | 86.40 | 80.25 | 98.47 | 72.88 |
Ensemble approach | ||||
 Voting classifier | 85.60 | 83.30 | 90.91 | 79.66 |
 Extreme gradient boosting | 87.20 | 84.72 | 92.42 | 81.36 |