Road Accident Severity & Hospital Recommendations Using Deep Learning Techniques
DOI:
https://doi.org/10.69996/fmep.2025030Keywords:
Road Accident Severity, Hand Injury Detection, Deep Learning, Convolutional Neural Network (CNN), Support Vector Machine (SVM)Abstract
Road accidents remain a major public health concern, often resulting in severe injuries or fatalities due to delayed emergency response and inadequate hospital selection. This research proposes an intelligent deep learning framework for predicting the severity of road accident-related hand injuries and recommending the nearest suitable hospitals for treatment. Utilizing convolutional neural networks (CNN) alongside traditional classifiers such as Support Vector Machine (SVM), Decision Tree, and Random Forest, the model is trained on real-time image datasets to ensure accurate classification of injury severity. Performance evaluation using accuracy, precision, recall, and F1-score demonstrates that CNN and Random Forest classifiers achieve superior results. The system is implemented in a Jupyter Notebook environment, where comprehensive visualization of image data, class distribution, and confusion matrices enables model interpretability. Furthermore, the integration of a hospital recommendation module based on predicted severity supports efficient decision-making during critical emergency situations. This study underlines the potential of combining AI and healthcare for enhancing accident response systems, thereby contributing to the advancement of intelligent healthcare and smart city applications.
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