Project Overview:

The Health Data Visualization Dashboard is a responsive web application that collects, processes, and visually presents health-related data in the form of interactive charts, graphs, and tables. This system enables healthcare providers, patients, and researchers to analyze trends, track metrics, and make data-driven decisions related to individual or public health.

The dashboard supports integration with health devices (via IoT), manual data entry, or uploaded health records.

Technologies Used:

• Frontend: HTML, CSS, Bootstrap, JavaScript (with Chart.js, D3.js or Google Charts)

• Backend: PHP / Java / Node.js

• Database: MySQL / MongoDB

• Optional: API integrations with fitness trackers or medical devices

Core Features:

1. User Roles:

• Admin: Manage users, data sources, and view system-wide analytics.

• Patient/User: Upload/view personal health records and visual data.

• Doctor/Analyst: Access patient dashboards, observe trends, give advice.

2. Data Sources:

• Manual entry of vitals (e.g., blood pressure, glucose levels).

• Upload CSV/Excel medical data.

• API integration with wearables like Fitbit or smartwatches (optional IoT).

3. Dashboard Modules:

• Vitals Tracker:

  • Daily/weekly tracking of blood pressure, heart rate, temperature, etc.

  • Line charts for trend analysis.

• Sleep & Activity Monitor:

  • Bar and pie charts for sleep hours, activity levels, steps taken, etc.

• Nutrition Overview:

  • Calorie intake and nutrient distribution shown using doughnut and radar charts.

• BMI & Weight Progress:

  • Track changes in weight, BMI with alerts if out of normal range.

• Medical Report Uploads:

  • Secure document uploads with optional visualization of values (like cholesterol, hemoglobin).

4. Interactive Visualizations:

• Drag-and-drop data panels.

• Real-time updates with WebSocket (optional).

• Filter by time range (day, week, month, year).

• Export charts as images or PDF.

5. Analytics & Insights:

• Auto-generated health summaries.

• Warnings for critical values (e.g., very high BP).

• Predictive analytics for risk detection (e.g., risk of diabetes using ML – optional).

6. Security & Privacy:

• Secure login/authentication.

• Role-based data access.

• Optional data encryption for uploads.