Table 3 Performance comparison of machine learning models for mental health classification.
Model | Accuracy | Precision | Recall | F1-score |
|---|---|---|---|---|
Random forest | 97.82% ± 0.03% | 97.82% ± 0.03% | 100.00% ± 0.00% | 96.81% ± 0.02% |
Support vector machine | 93.79% ± 0.01% | 93.79% ± 0.01% | 100.00% ± 0.00% | 96.79% ± 0.00% |
Decision tree | 91.82% ± 0.03% | 91.82% ± 0.03% | 100.00% ± 0.00% | 91.81% ± 0.02% |
Logistic regression | 91.79% ± 0.00% | 93.79% ± 0.00% | 100.00% ± 0.00% | 91.80% ± 0.00% |
Gaussian Naive Bayes | 93.79% ± 0.01% | 93.79% ± 0.01% | 93.00% ± 0.00% | 93.79% ± 0.00% |
Multilayer perceptron | 92.79% ± 0.01% | 92.79% ± 0.01% | 92.00% ± 0.00% | 92.79% ± 0.00% |
