Project Overview:

The Online Health Risk Predictor is a smart web-based application that uses user-inputted health data and machine learning models to predict potential health risks like diabetes, heart disease, obesity, or hypertension. The goal is to assist individuals in early detection and prevention by offering risk assessment based on clinical parameters.

This system can be integrated into health platforms, hospitals, or used independently by individuals concerned about their health status.

Technologies Used:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: PHP / Java / Node.js

• Machine Learning (Model Training): Python (with scikit-learn or TensorFlow)

• Database: MySQL / MongoDB

• API Communication: REST API to connect ML model with frontend/backend

Core Features:

1. User Registration & Login:

• Secure sign-up and login with role-based access.

• Option to save historical predictions for registered users.

2. Health Data Input Form:

• Collect user details such as:

  • Age, gender, weight, height (BMI)

  • Blood pressure

  • Glucose level

  • Cholesterol

  • Smoking/alcohol consumption habits

  • Family history of diseases

3. Machine Learning-Based Prediction:

• Trained models (e.g., logistic regression, decision trees, or neural networks) used to:

  • Predict disease risks (like diabetes, heart disease, etc.)

  • Output probability score (e.g., "You have a 78% risk of heart disease")

4. Results Dashboard:

• Show prediction result with risk levels: Low, Moderate, High

• Provide visual graphs of user's vitals

• Show comparison with healthy ranges

5. Health Tips & Recommendations:

• Auto-generate personalized advice to lower health risk

• Display relevant articles, diet tips, exercise suggestions, etc.

6. Data Security & Privacy:

• All health data is securely stored

• Role-based access to protect sensitive information

7. Admin Panel:

• Manage users, view overall statistics

• Update risk models (new disease models can be uploaded)