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Optimizing ambulance location based on road accident data in Rwanda using machine learning algorithms.

Gatembo Bahati1, Emmanuel Masabo2,3

  • 1African Center of Excellence in Data Science (ACE-DS), College of Business and Economics, University of Rwanda, 4285, Kigali, Rwanda. gatembobahati@gmail.com.

International Journal of Health Geographics
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Summary
This summary is machine-generated.

Machine learning optimized ambulance placement in Rwanda by analyzing road accident data. This approach identified 58 key locations, significantly improving emergency response times and potentially saving lives.

Keywords:
Emergency response timeHotspots for ambulance locationMachine learningRoad accident

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Area of Science:

  • Public Health
  • Data Science
  • Operations Research

Background:

  • Road accidents in Rwanda are a significant cause of injury and death, with increasing trends observed in recent years.
  • Timely emergency medical response is critical for improving survival rates in accident situations.
  • Strategic ambulance placement is essential for reducing response times in high-frequency accident regions.

Purpose of the Study:

  • To optimize ambulance locations in Rwanda using machine learning algorithms based on road accident data.
  • To identify critical areas (hotspots) for ambulance deployment to minimize emergency response times.
  • To leverage advanced analytical techniques for enhancing emergency medical services.

Main Methods:

  • Utilized machine learning, specifically the random forest model, to predict emergency response times.
  • Employed k-means clustering combined with linear programming to determine optimal ambulance station locations.
  • Integrated road accident data with administrative boundary shapefiles for spatial analysis.

Main Results:

  • The random forest model achieved 94.3% accuracy in classifying emergency response times.
  • Identified 58 optimal ambulance hotspots across Rwanda.
  • The average distance from an ambulance station to the nearest accident location was optimized to 1092.773 meters.

Conclusions:

  • Machine learning models can uncover insights beyond traditional statistical methods for optimizing resource allocation.
  • The developed model demonstrates strong performance in optimizing ambulance locations using road accident data.
  • This data-driven approach can significantly enhance the efficiency of emergency medical services in Rwanda.