Project Overview
An intelligent web application that leverages supervised machine learning algorithms to predict diseases such as diabetes, heart disease, and cancer based on patient-provided symptoms and medical history data.
Objectives
- Develop a reliable ML model with >90% accuracy
- Create an intuitive patient-facing interface
- Enable real-time disease risk assessment
- Provide actionable health recommendations
Features & Modules
Multi-disease prediction (Diabetes, Heart, Cancer, etc.)
Interactive symptom input form
Risk percentage dashboard
Medical history tracking
Doctor recommendation system
PDF report generation
Technology Stack
Frontend
ReactTailwind CSSChart.js
Backend
PythonFlaskScikit-learn
Database
MySQL
Hardware Requirements
- Intel i5 or higher
- 8GB RAM
- 50GB Storage
Software Requirements
- Python 3.9+
- Node.js 18+
- MySQL 8.0
- VS Code
Future Scope
- Integration with wearable IoT devices
- Mobile app development
- Telemedicine consultation
Quick FAQs
Is this an IEEE project?
Yes, this project is based on published IEEE research papers.
What accuracy does the model achieve?
The model achieves 91–95% accuracy depending on the disease type.
Technologies Used
PythonFlaskMachine LearningReactMySQL