Oral Cancer Risk Analysis API A dynamic Flask-based Web API designed for healthcare analytics, enabling real-time querying of patient demographics and risk factors from a 2GB SQLite database. Built for rapid prototyping and backend precision, this project demonstrates modular API design, secure database connectivity, and efficient data retrieval for clinical decision support.
Purpose & Functionality This API empowers healthcare professionals and researchers to:
- Query patient demographics by age, gender, and name
- Analyze risk factors (alcohol intake, paan, smoking, tobacco chewing) across age groups
- Support resource planning through age-wise and gender-wise patient counts
- Ensure data integrity via secure adapter-based connection and runtime filtering
| Category | Description |
|---|---|
| Adapter Connection | Secure SQLite3 adapter with cursor-based execution for reliable query management and transaction handling. |
| Runtime GET Requests | Dynamic RESTful endpoints accepting user input via query parameters for real-time data filtering. |
| Risk Factor Analysis | Aggregates patients who answered “No” to specific habits (alcohol intake, paan, smoking, tobacco chewing). |
| Demographic Insights | Provides age-wise and gender-wise patient counts to support data-driven healthcare planning. |
| Data Integrity | Ensures database consistency and protection using parameterized SQL queries to prevent injection vulnerabilities. |
| Troubleshooting & Debugging | Applied debugging steps such as verifying database connection strings, validating cursor objects, and testing adapter responses to maintain stable API–database communication. |
🧪 SQL Integration Highlights This project uses embedded SQL queries within Python to: Store & Retrieve: SELECT statements for patient data and schema introspection Manipulate Data: Filtered queries for age and habit-based segmentation Transaction Safety: Cursor-based execution ensures atomicity and rollback safety Content Management: Structured JSON responses for frontend or analytics pipelines Embedded SQL: Direct SQL statements within Flask routes for rapid prototyping
📁 API Endpoints | Endpoint | Method | Description | | / | GET | Health check for API | | /schema | GET | View table schema | | /PatientNames?n=5 | GET | Get top N patient names | | /PatientNames_forAge?age=40 | GET | Filter patient names by age | | /patientcount_forAge?age=40 | GET | Count patients of a specific age | | /Agewisepatients | GET | Age-wise patient distribution | | /Genderwisepatients | GET | Gender-wise patient distribution | | /risk_factors | GET | Age-wise count of patients who answered "No" to habits |
The project highlights originality in risk factor segmentation and demographic filtering
##Bottom-line of my SQL-driven backend application: I avoided using a global connection string as Each route establishes its own scoped database connection via get_db_connection(), ensuring thread safety, minimizing resource leaks, and preserving transactional integrity across concurrent requests. This design choice aligns with best practices for scalable, production-grade Flask APIs.