💡 Features
- Step-by-step input form: Personal ➜ Medical
- Trained Decision Tree classifier (sklearn)
- Dark-themed, animated UI
- Full summary of user inputs and prediction
- Option to download PDF report
- Confusion matrix visualization during model training
🛠️ Tech Stack
- Python, Flask, Sklearn
- Decision Tree Classifier
- Jinja2 Templates, HTML/CSS
- FPDF2 (for PDF export)
📈 Results and Evaluation
User inputs were processed through the model, and the following results were obtained:
Achieved high classification accuracy on test data, with balanced precision and recall. Confusion matrix insights enabled deeper model evaluation.
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| No Disease (0) | 0.80 | 0.75 | 0.77 | 32 |
| Disease (1) | 0.73 | 0.79 | 0.76 | 28 |
| Accuracy | 0.77 | |||
| Macro Avg | 0.77 | 0.77 | 0.77 | 60 |
| Weighted Avg | 0.77 | 0.77 | 0.77 | 60 |
