Tubercool
Health AI / ML (2025)Domain: Public HealthModel: Trained MLPurpose: Early Diagnosis
Tubercool
Early tuberculosis diagnosis through trait analysis and trained ML models.
Product Interface
The Big Picture
What I Built
A lightweight medical diagnostic tool designed for clinics in rural areas. Healthcare workers input patient symptoms to quickly assess tuberculosis risk and prioritize care.
What Makes It Cool
- ✦Built to assist doctors and nurses in low-resource medical clinics
- ✦Fast risk evaluation from simple symptom inputs
- ✦Clean, easy-to-use form interface
- ✦Developed using clinical health data models
Real-World Impact
Why This Matters
TB diagnosis in low-resource settings is slow and often inaccurate. Tubercool uses a trained ML model on microbial TB data to analyse symptoms and suggest early diagnostic outcomes.
Key TakeawayBuilt with clean software principles: fast loading, zero unnecessary clutter, and privacy first.
Behind The Scenes
How It Works
Patient health data stays completely confidential and is never stored on external servers.
Tested for speed, reliability, and smooth interactions
Stack & Technologies
The Toolkit
The tools, frameworks, and engines picked specifically for this project:
PythonScikit-learnFlaskMLHealthcare Data
Want to talk about this project or collaborate?
I'm always open to discussing new software ideas, apps, and opportunities.