AI Medical Compendium Journal:
International journal of injury control and safety promotion

Showing 1 to 10 of 14 articles

Identifying factors affecting crash injury severity of pillion riders using interpretable machine learning techniques.

International journal of injury control and safety promotion
In India, motorized two-wheeler (TW) riders account for 44.5% of fatal road crashes. While factors affecting drivers have been studied, research on pillion riders' injury severity remains limited. The study aims to identify factors causing severe inj...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

International journal of injury control and safety promotion
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...

Interpretable machine learning for evaluating risk factors of freeway crash severity.

International journal of injury control and safety promotion
Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement ...

Identification of the best machine learning model for the prediction of driver injury severity.

International journal of injury control and safety promotion
Predicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. Thes...

Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types.

International journal of injury control and safety promotion
Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activitie...

Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework.

International journal of injury control and safety promotion
With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering proc...

Investigating the effect of road condition and vacation on crash severity using machine learning algorithms.

International journal of injury control and safety promotion
Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial an...

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study.

International journal of injury control and safety promotion
A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlati...

Automatic fall detection using region-based convolutional neural network.

International journal of injury control and safety promotion
The common classifiers usually used to detect fall incidents depend on building and maintaining complex feature extraction for accurate machine learning tasks. However, these efforts have not succeeded in determining an ideal classifier or feature ex...

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt.

International journal of injury control and safety promotion
The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequentl...