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Health Sense – Disease Prediction System

Project Overview
  • Project Title: Health Sense – Disease Prediction System
  • Domain: HealthCare
  • Technology Stack: Java 17, Spring Data JPA, MySQL, HTML, CSS, React
  • Duration: 3 Weeks
Project Description

HealthSense is an intelligent web platform that allows users to input symptoms or health parameters and receive potential disease predictions using trained machine learning models. It leverages data science to assist in early detection and decision support, providing insights into possible conditions and preventive care.

Key Objective
  • Predict probable diseases based on user-input symptoms or diagnostic parameters. (The platform acts like an intelligent "first checkup" based on symptoms, giving users an idea of what disease they could possibly have before seeing a doctor.)
  • Empower users with early warning for timely diagnosis or medical consultation. (helping users to get an early idea about their possible health problems so they can visit a doctor quickly and get the right treatment on time)
  • Reduce manual diagnostic errors by using trained ML models (machine learning models to help avoid mistakes that can happen when only humans check symptoms, so the predictions are more accurate and reliable)
  • Provide clean UI/UX for non-technical users to access health insights. (designing the app to be simple, neat, and easy to use so that even people without any technical knowledge can easily check their health results)
  • Enable doctors to validate and enhance prediction accuracy using feedback. (allowing doctors to review the system’s predictions, give corrections or feedback, and help improve the model so it becomes even more accurate over time)
  • Store user prediction history and trends for long-term tracking.
  • (saving each user’s past predictions so they can see how their health has changed over time and track patterns or improvements.)
  • Offer educational health recommendations based on predicted conditions.
Core Feature
  • User Registration & Login System.
  • Symptom checker with multi-select input (application will show a list of symptoms, and users can select more than one symptom at the same time (like fever + cough + headache) instead of choosing only one.)
  • Disease prediction using ML classification model.
  • Interactive dashboard with prediction results, confidence score, and prevention tips
  • Patient health history tracking and downloadable reports.
  • Doctor/admin panel to view usage trends and model feedback.
  • Smart feedback loop for improving model accuracy.
  • Notifications and Alerts.

Tools & Technologies Used

Category Tools / Technologies
Language Java 17
Framework SpringBoot
ORM Spring Data JPA
Machine Learning (ML) Python(Scikit-learn / TensorFlow / Pandas).
ML Integration REST APIs using Python Flask or FastAPI.
ML Deployement Dockerized Python microservices
Notification Services Java Mail (Email), Twilio (SMS)
File Storage AWS S3
Scheduler & Reminders Spring Scheduler + Twilio / Email API
File & Record System AWS S3 / Local FS (for prescriptions, reports).
API Documentation Swagger / SpringDoc OpenAPI
Authentication & Security Spring Security + JWT + OAuth2
Logging Log4j
Database MySQL
FrontEnd React.js
Visualization & Reporting Chart.js / Apache POI / JasperReports.
Notificatins Java Mail, Twilio SMS
Building Tools & Dependencies Maven, Docker, Git, Jenkins
Testing Mockito, Postman (API Test)
Cloud and Deployment AWS EC2, Docker, RDS (MySQL)

Prerequisites

💻 System Requirements
  • Operating System: Windows / macOS / Linux
  • RAM: Minimum 8 GB (Recommended 16 GB for running ML microservices and backend in parallel)
  • Java JDK 17 installed and environment variables configured
  • Python 3.x installed (for ML model development & integration)
  • Node.js and npm/yarn for React.js frontend
  • Docker installed for running ML services as containers
  • MySQL Server and Redis installed/configured
📦 Software Dependencies
  • Java 17 with Spring Boot
  • Spring Data JPA for ORM and database operations
  • Python libraries: scikit-learn, pandas, Flask/FastAPI for ML services
  • Spring Security with JWT for secure access control
  • Redis for caching prediction data or session management
  • Chart.js / JasperReports / Apache POI for visualizing health trends and reports
  • AWS S3 for storing documents like health reports
  • Swagger or SpringDoc OpenAPI for API documentation
  • Java Mail and Twilio for sending notifications
  • Maven for dependency and project build management
  • Postman for REST API testing
🧰 Tools & Services
  • IDE: IntelliJ IDEA or Eclipse for backend, VS Code for frontend and Python ML scripts
  • Git and GitHub/GitLab for version control
  • Jenkins for CI/CD integration and deployment
  • AWS EC2 for hosting Java and Python microservices
  • AWS RDS (MySQL) and AWS S3 (file storage)
  • Docker for containerizing and deploying Python-based ML microservices
🌐 Technical Knowledge
  • Experience in Java Spring Boot for API development
  • React.js basics for building the frontend symptom-checker UI
  • Python ML model development using scikit-learn/TensorFlow
  • REST API development in Python using Flask or FastAPI
  • Docker for containerizing ML services
  • Integration of React with Java and Python APIs
  • Authentication and security using JWT
  • Database schema design and query optimization in MySQL
  • Knowledge of Redis for caching and performance tuning
  • Experience using Postman for testing and Swagger for documentation
  • TechnologyJava
  • TypeWeb Application
  • Duration3 weeks
  • ModeOnline/Offline
  • CertificateYes
  • Project ReviewIncluded
  • Doubt SupportLive & Chat Support
  • Career SupportResume & Interview Tips
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