Mengistu Abraham Keffale, Gedefaw Andualem Enyew, Baykemagn Nebebe Demis, Walle Agmasie Damtew, Yehuala Tirualem Zeleke, Alemayehu Meron Asmamaw, Messelu Mengistu Abebe, Assaye Bayou Tilahun
Department of Health Informatics, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia.
Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Sci Rep. 2025 Jul 1;15(1):21913. doi: 10.1038/s41598-025-08005-2.
Road traffic accidents (RTAs) in Northwest Ethiopia, a region with a fatality rate of 32.2 per 100,000 residents, pose a critical public health challenge exacerbated by infrastructural deficits and environmental hazards. This study leverages machine learning (ML) to predict accident severity, addressing gaps in localized predictive frameworks for low- and middle-income countries (LMICs). Our study aims to predict the severity of car accidents in Northwest Ethiopia via machine-learning techniques. Using a dataset of 2,000 accidents (2018-2023) from police reports, we integrated driver demographics, behavioral factors (e.g., alcohol use, seatbelt compliance), and environmental conditions (e.g., unpaved roads, weather) in North West Ethiopia. Ten ML models, including Random Forest, XGBoost, and LightGBM, were evaluated after addressing class imbalance via the Synthetic Minority Oversampling Technique (SMOTE). Hyperparameter tuning and Shapley Additive explanations (SHAP) provided model optimization and interpretability. Random Forest outperformed other models, achieving 82% accuracy (AUC-ROC: 0.87) post-tuning. Driver age (mean: 44 years) and environmental factors (e.g., nighttime on unlit roads, rainy conditions) were critical predictors, increasing fatal accident likelihood by 62%. SMOTE improved the accuracy of the outperforming random forest accuracy from 78.6 to 82%. Random Forest exhibited the highest recall (0.82) after optimization, while ensemble methods dominated performance metrics. The study underscores the efficacy of ML in contextualizing accident severity in LMICs, with Random Forest emerging as a robust tool for policymakers. Prioritizing road paving, sobriety checkpoints, and motorcycle safety could mitigate risks, aligning with Sustainable Development Goal 3.6. Future work should address data limitations (underreporting, geospatial gaps) and expand model interpretability.
埃塞俄比亚西北部的道路交通事故（RTAs）是一个严峻的公共卫生挑战，该地区每10万居民的死亡率为32.2，基础设施不足和环境危害加剧了这一问题。本研究利用机器学习（ML）来预测事故严重程度，以填补低收入和中等收入国家（LMICs）本地化预测框架的空白。我们的研究旨在通过机器学习技术预测埃塞俄比亚西北部汽车事故的严重程度。利用警方报告中的2000起事故（2018 - 2023年）数据集，我们纳入了埃塞俄比亚西北部的驾驶员人口统计学特征、行为因素（如酒精使用、安全带佩戴情况）和环境条件（如未铺砌道路、天气）。在通过合成少数过采样技术（SMOTE）解决类别不平衡问题后，对包括随机森林、XGBoost和LightGBM在内的10种ML模型进行了评估。超参数调整和Shapley附加解释（SHAP）提供了模型优化和可解释性。随机森林在调整后优于其他模型，准确率达到82%（AUC - ROC：0.87）。驾驶员年龄（平均：44岁）和环境因素（如夜间在无照明道路上、下雨情况）是关键预测因素，将致命事故可能性增加了62%。SMOTE将表现最佳的随机森林准确率从78.6%提高到了82%。优化后随机森林的召回率最高（0.82），而集成方法在性能指标方面占主导地位。该研究强调了ML在低收入和中等收入国家将事故严重程度情境化方面的有效性，随机森林成为政策制定者的有力工具。优先进行道路铺设、设立清醒检查站和提高摩托车安全性可以降低风险，这与可持续发展目标3.6相一致。未来的工作应解决数据限制（报告不足、地理空间差距）并扩大模型的可解释性。
