Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Generative Artificial Intelligence for Environmental Assessment: A New Paradigm for Sustainability Analysis.

Environmental management·2026
Same author

Symbiosis, zero-waste goal and resource-sharing potential for UAE industries.

Journal of environmental management·2025
Same author

Microclimate Performance Analysis of Urban Vegetation: Evidence from Hot Humid Middle Eastern Cities.

Plants (Basel, Switzerland)·2025
Same author

Predicting few disinfection byproducts in the water distribution systems using machine learning models.

Environmental science and pollution research international·2025
Same author

Albizia Procera-Derived Nitrogen-Doped Carbon: A Versatile Material for Energy Conversion, Storage, and Environmental Applications.

Chemistry, an Asian journal·2025
Same author

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent.

Journal of environmental management·2024

Related Experiment Video

Updated: Dec 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising

Khaled Assi1, Syed Masiur Rahman2, Umer Mansoor1

  • 1Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

International Journal of Environmental Research and Public Health
|August 6, 2020
PubMed
Summary

Machine learning models accurately predict traffic crash injury severity. The Support Vector Machine with Fuzzy C-Means clustering (SVM-FCM) model demonstrated superior performance in predicting severe and non-severe crash outcomes.

Keywords:
crash injury severityemergency managementfeedforward neural networks (FNN)fuzzy c-means clustering (FCM)machine learningsupport vector machines (SVM)

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.3K

Related Experiment Videos

Last Updated: Dec 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.3K

Area of Science:

  • Traffic safety research
  • Machine learning applications in accident analysis
  • Injury severity prediction

Background:

  • Predicting traffic crash injury severity is vital for mitigating road accident consequences.
  • Effective prediction enables trauma centers to prepare for victim treatment.
  • Utilizing easily identifiable crash site data enhances predictive accuracy.

Purpose of the Study:

  • To develop and compare machine learning models for predicting traffic crash injury severity.
  • To evaluate the effectiveness of fuzzy c-means clustering in enhancing prediction models.
  • To identify the best-performing model for real-time injury severity prediction.

Main Methods:

  • Developed four machine learning models: Feed-Forward Neural Networks (FNN), Support Vector Machine (SVM), FNN-FCM, and SVM-FCM.
  • Employed fuzzy c-means (FCM) clustering to create separate models for distinct crash clusters.
  • Utilized 15 crash-related parameters, including vehicle and road attributes, from Great Britain's 2011-2016 crash database.

Main Results:

  • The Support Vector Machine with Fuzzy C-Means clustering (SVM-FCM) model achieved the highest accuracy and F1 score.
  • FCM clustering significantly improved the predictive capabilities of both FNN and SVM models.
  • All developed models were evaluated based on accuracy, sensitivity, precision, and F1 score.

Conclusions:

  • The SVM-FCM model is highly effective for predicting traffic crash injury severity.
  • Fuzzy C-Means clustering is a valuable technique for enhancing machine learning-based crash prediction.
  • The study highlights the potential of using readily available crash data for improved trauma care planning.