A conceptual demo platform that leverages machine learning to assess a person's 5-year cardiovascular risk from basic biometric data — designed for the Uzbekistan healthcare context.
Cardiovascular diseases (CVD) are the leading cause of death in Uzbekistan, accounting for 57.2% of all recorded deaths in the first 9 months of 2024. Key systemic challenges include:
- Only 29 cardiologists per million people — far below European standards
- CVD is increasingly affecting younger age groups (16–18)
- Limited access to high-tech diagnostic equipment in regional areas
- No scalable early-warning tool for primary care settings
CardioScan is an AI-based risk assessment platform that takes 8–12 basic patient inputs (age, smoking status, blood pressure, lab results, etc.) and outputs a 0–100 Cardiovascular Risk Index for the next 5 years.
| Feature | Description |
|---|---|
| Risk Prediction | Gradient Boosting model estimates 5-year CVD risk from routine clinical data |
| Explainable AI (XAI) | SHAP-based feature importance shows why the model gave a certain score |
| Personalized Recommendations | Gemini API generates lifestyle, diet, and exercise advice tailored to the risk level |
| Triage Support | Flags high-risk individuals for urgent cardiology referral |
| Layer | Technology |
|---|---|
| Frontend | React, Tailwind CSS |
| Backend | Python (Flask / Django) |
| Database | Google Cloud Firestore |
| ML Model | Scikit-learn, Pandas — Gradient Boosting Classifier |
| Generative AI | Gemini API |
| Explainability | SHAP |
Patient inputs basic data
↓
Gradient Boosting model generates Risk Index (0–100)
↓
SHAP explains the top contributing risk factors
↓
Gemini API generates personalized health recommendations
↓
High-risk patients are flagged for cardiology referral
Risk Index: 78% — HIGH RISK GROUP
Top contributing factors:
Age (55): ↑ 45% influence
Current smoker: ↑ 30% influence
Blood pressure 145/95: ↑ 15% influence
AI Recommendation:
"We recommend quitting smoking immediately and engaging in at least
150 minutes of brisk walking per week. This could significantly
reduce your cardiovascular risk."
Phase 1 (Current) — Concept & Demo
└─ Problem definition, PoC, AI simulation demo
Phase 2 — First AI Model & Testing
└─ Dataset collection, model training, initial evaluation
Phase 3 — MVP Platform
└─ Backend API + React frontend integration
└─ User auth + Firestore result storage
Phase 4 — Pilot & Clinical Validation
└─ Validation with local cardiologists
└─ Pilot deployment in medical institutions
⚠️ This is a conceptual demo built for the AI500 competition. The risk scores shown are simulated and not intended for real medical use.
Baqoyev Feruzbek — Solo Developer Skills: Python (AI/Backend), React (Frontend)
CVD-related deaths in Uzbekistan are the primary social driver behind this project. The goal is to build something genuinely useful and deployable — not just a demo, but a foundation for a real clinical tool.
CardioScan — Advancing Preventive Medicine with Innovative Solutions Built for the AI500 Competition · 2025