Findings and Visualizations¶
Summary of Experimental Results¶
This page summarizes the key quantitative findings and qualitative observations from the dual-model framework for depression and anxiety severity prediction.
The evaluation is performed using a stratified 70 percent training split and 30 percent test split to preserve severity-level proportions.
Model Performance¶
- Depression Severity Model
Test accuracy: 99.87 percent
Near-perfect precision, recall, and F1-score across severity levels.
- Anxiety Severity Model
Test accuracy: 98.93 percent
Strong performance overall with small reductions in higher severity categories.
- Discriminative Power
AUC results confirm strong separation across classes.
Depression remains near-perfect. Anxiety shows a minor reduction in higher classes.
Classification Report¶
Depression Prediction¶
| Class | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| 0 | 1.00 | 1.00 | 1.00 | 256 |
| 1 | 1.00 | 1.00 | 1.00 | 203 |
| 2 | 1.00 | 1.00 | 1.00 | 166 |
| 3 | 0.99 | 1.00 | 1.00 | 125 |
| Macro Avg | 1.00 | 1.00 | 1.00 | 750 |
| Weighted Avg | 1.00 | 1.00 | 1.00 | 750 |
Anxiety Prediction¶
| Class | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| 0 | 1.00 | 1.00 | 1.00 | 260 |
| 1 | 1.00 | 1.00 | 1.00 | 204 |
| 2 | 0.97 | 0.98 | 0.98 | 170 |
| 3 | 0.97 | 0.96 | 0.97 | 116 |
| Macro Avg | 1.00 | 1.00 | 1.00 | 750 |
| Weighted Avg | 1.00 | 1.00 | 1.00 | 750 |
AUC Comparison¶
| Class | AUC Depression | AUC Anxiety |
|---|---|---|
| 0 (None) | 1.00 | 1.00 |
| 1 (Mild) | 1.00 | 1.00 |
| 2 (Moderate) | 1.00 | 0.98 |
| 3 (Severe) | 0.99 | 0.97 |
| Macro Avg | 1.00 | 0.99 |
| Weighted Avg | 1.00 | 0.99 |
Embedding Space Visualizations¶
- Binary Separation Views
Two-dimensional embedding projections show clustering patterns for severe versus none classes for both depression and anxiety.
- UMAP Projections
UMAP projections display a graded continuum across all four severity levels, with partial overlap expected for continuous constructs.
Add your figures here:
Figure 1Embedding space for depression, none vs severeFigure 2Embedding space for anxiety, none vs severeFigure 3UMAP projection for depression severityFigure 4UMAP projection for anxiety severity
Linguistic Pattern Observations¶
Word-frequency analysis reveals severity-associated language shifts:
- Lower severity levels show more neutral, routine daily language.
- Moderate and severe levels show increased negative and self-focused vocabulary.
- Anxiety shows stronger patterns of fear, worry, and physiological stress language at higher severity.
Add your tables here:
Table 1Depression word frequency by severityTable 2Anxiety word frequency by severity
Key Observations¶
- Errors Are Usually Adjacent
Misclassifications occur primarily between neighboring severity categories such as moderate and severe.
- Higher Severity Anxiety Is Harder
Anxiety shows slightly reduced recall in severe class, consistent with overlap in language across high severity states.
- High-Risk Separation Is Strong
Severe cases are rarely misclassified as none, supporting reliability for high-risk identification.
Example Outputs¶
The following structure can be used to present example predictions from the prototype:
- Input
Arabic text entry
- Output
Depression score and anxiety score from 0 to 3
- Alerts
High score, worsening trend, or sudden spike if history is provided
Include only synthetic or fully anonymized examples.
Connection to Alert Framework¶
The predictive alert mechanism converts severity outputs into longitudinal risk signals by detecting:
- sustained high severity
- gradual worsening trends
- sudden spikes
For details, refer to the alert framework section in the paper and the deployment documentation.