Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance.
In order to better understand this issue, Dr. Samar Al-Hajj from the Faculty of Health Sciences at the American University of Beirut (AUB), joined efforts with Huda Hammoud from the Maroun Semaan Faculty of Engineering and Architecture at AUB and Dr. Ali Ghandour from the National Council for Scientific Research to design a model that adopted a hybrid ensemble machine learning classifier to identify risk factors contributing to fatal road injuries. Based on model testing, seven variables were significantly associated with fatality occurrence, including crash type, injury severity, spatial cluster-ID, and crash time (hour).
Evidence from this study should be translated into safety programs and enhanced road policies.
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