Abstract
This study analyzes data from the National Highway Trafc Safety Administration’s 2021 Crash Report Sampling System to identify key factors contributing to the severity of injuries in car accidents. By utilizing various machine learning algorithms and cross-validation techniques, we assessed metrics such as accuracy, sensitivity, precision, specifcity, and the area under the curve (AUC) to evaluate the efectiveness of predictive models. All data preprocessing and model building was done using KNIME Analytical software [9]. Our fndings reveal signifcant correlations between certain variables such as airbag injection, weather conditions, intoxication, vehicle state, driver distractions, and injury severity. These insights underscore the importance of stringent safety measures, including proper restraint system usage and advanced driver-assistance technologies, in reducing the risk of severe injuries in car accidents. Recommendations for policy enhancements and preventive measures are discussed to improve overall vehicle safety.
Semester
Summer 2024
STARS Citation
Agbemade, Emil and Kongyir, Benedict, "Predicting Road Accident Injury Severity for Drivers in Automobile Crashes in United States Using Machine Learning Models and AI" (2024). Data Science and Data Mining. 24.
https://stars.library.ucf.edu/data-science-mining/24
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