Project Overview:

The Patient Visit Forecasting System is a data-driven web application designed to predict the number of patient visits to hospitals or clinics based on historical data and external factors like season, weather, and regional disease outbreaks. It uses machine learning to help healthcare facilities optimize resource allocation, reduce wait times, and improve patient care efficiency.

This system is especially valuable for hospital administrators, clinic managers, and government health planners.

Technologies Used:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Node.js / PHP / Java

• Machine Learning (Model Training): Python (Pandas, scikit-learn, Prophet)

• Database: MySQL / MongoDB

• Visualization: Chart.js or D3.js

• API Communication: RESTful API

Key Features:

1. Admin Panel:

• Add hospital/clinic branches

• Upload historical data (CSV or manual entry)

• View forecasting reports

2. Historical Data Upload:

• Accepts data like:

  • Daily/weekly/monthly patient count

  • Department-wise visit numbers (e.g., Cardiology, Pediatrics)

  • Weather info (optional)

  • Holiday/calendar events

3. Forecasting Engine:

• Uses Time Series models like:

  • ARIMA

  • Facebook Prophet

  • LSTM (optional)

• Forecast patient visits for the next 7, 14, or 30 days

• Option to filter predictions by department

4. Data Visualization Dashboard:

• Line graphs for patient trends

• Bar charts comparing predicted vs actual

• Heatmaps showing peak hours/days

5. Custom Alerts & Recommendations:

• Get alerts if forecast exceeds certain thresholds

• Suggested staffing levels and resource adjustments

6. Downloadable Reports:

• Export daily or monthly forecasts in PDF/Excel format

7. User Roles:

• Admin: Full access to upload, manage, forecast

• Doctor/Staff: View relevant department data only