  Road traffic injury accounts for a substantial human and economic burden
globally. Understanding risk factors contributing to fatal injuries is of
paramount importance. In this study, we proposed a model that adopts a hybrid
ensemble machine learning classifier structured from sequential minimal
optimization and decision trees to identify risk factors contributing to fatal
road injuries. The model was constructed, trained, tested, and validated using
the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash
incidents, with fatality occurrence as the outcome variable. A sensitivity
analysis was conducted to examine the influence of multiple factors on fatality
occurrence. Seven out of the nine selected independent variables were
significantly associated with fatality occurrence, namely, crash type, injury
severity, spatial cluster-ID, and crash time (hour). Evidence gained from the
model data analysis will be adopted by policymakers and key stakeholders to
gain insights into major contributing factors associated with fatal road
crashes and to translate knowledge into safety programs and enhanced road
policies.
