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.

翻译：了解造成致命伤害的风险因素至关重要。在本研究中,我们提出了一个模型,采用混合混合混合混合机学习分类,由顺序最低优化和决策树组成,以找出造成致命道路伤害的风险因素;该模型是利用黎巴嫩公路事故平台数据库(LRAP)建立、培训、测试和验证的,共有8 482起道路事故,其发生为结果变量,其中死亡事件为8482起;进行了敏感性分析,以审查多重因素对死亡发生率的影响;在选定的9个独立变量中,有7个与死亡发生率密切相关,即坠毁类型、伤害严重程度、空间集束识别和碰撞时间(小时);决策者和主要利益攸关方将采用数据模型分析获得的证据,以深入了解与致命道路事故有关的主要促成因素,并将知识转化为安全方案和加强的道路政策。