Paper Title
Data-Centric Approach to Road Crash Severity Estimation Using Recursive Feature Selection and Deep Learning
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Registration ID: IJNRD_322368
Published ID: IJNRD2604163
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Keywords
Deep learning, Feature selection, Road accidents, Random Forest, Recursive Feature Elimination
Abstract
Injuries and deaths in the world are among the main causes of road accidents. Timely and correct emergency assistance is required to ensure lives are spared. Emergency response is important in saving lives because road accidents still remain as one of the principal causes of injuries and deaths in the world. Conventional analysis of accident severity makes use of manual reporting which may lead to delays in reporting and lead to the impediment of the real-time decision making. This research paper proposes a hybrid algorithm combining the use of Random Forest-Recursive Feature Elimination (RF -RFE) with a deep learning system, as a solution to this issue. RF-RFE selects the most significant characteristics, including vehicles information, the kind of the accident, weather, and demographics. These enhanced features categorize the deep learning model as mild, moderate or severe accidents. The new model, which is tested faster and more accurate than ancient machine-learning methods, indicates that the new avenue has clearly shown promising outcomes. In an emergency system, it may accelerate the speed of severity assessments, enhance the process of sharing resources and accelerate medical services, increasing survivor probability
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How To Cite (APA)
Syed Nurja, Saidu Soumya, Veldurthi Venkat Siddarth, & Vadde Chethana Priya (April-2026). Data-Centric Approach to Road Crash Severity Estimation Using Recursive Feature Selection and Deep Learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), b478-b482. https://ijnrd.org/papers/IJNRD2604163.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : b478-b482
Other Publication Details
Paper Reg. ID: IJNRD_322368
Published Paper Id: IJNRD2604163
Research Area: Other area not in list
Author Type: Indian Author
Country: Medchal , Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604163.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604163
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