Doctor AI

What this project is trying to make possible.

Demonstrate an end-to-end supervised machine-learning workflow, from symptom encoding and model training to probability-ranked output.

A plain-language view of the system.

1. The project loads 4,920 dataset records covering 41 labels.
2. It converts 132 symptoms into binary model features.
3. A Random Forest classifier learns symptom-to-label patterns.
4. Selected symptoms produce three ranked possibilities with supporting information.

What the project demonstrates today.

Educational and research use only. It is not a medical diagnosis, treatment, or emergency-care product.

Where the next useful versions could go.

• Independent clinical validation
• Uncertainty and bias reporting
• Multilingual interfaces
• Privacy and healthcare-compliance review