用三种模型预测寿命，随机森林最准且能解释关键因素
Interpretable Machine Learning for Life Expectancy Prediction: A Comparative Study of Linear Regression, Decision Tree, and Random Forest
- 比较线性回归、决策树和随机森林在寿命预测中的表现
- 随机森林 $R^2=0.9423$ 最高，显著优于其他模型
- 揭示疫苗接种率和艾滋病等是影响寿命的关键因素
寿命是衡量人口健康与社会经济福祉的核心指标，但受人口结构、环境与医疗等多重因素影响，准确预测仍具挑战。本研究基于世卫组织（WHO）与联合国（UN）的真实数据，评估了线性回归（LR）、回归决策树（RDT）和随机森林（RF）三种机器学习模型。经过对缺失值与不一致数据的广泛预处理后，采用 $R^2$、平均绝对误差（MAE）和均方根误差（RMSE）评估性能。结果表明，随机森林 $R^2=0.9423$，预测精度最高，显著优于其他模型。为提升可解释性，对线性回归使用 p 值，对树模型使用特征重要性，发现白喉、麻疹疫苗接种率及艾滋病、成人死亡率等人口特征是寿命预测的关键驱动因素。该研究凸显了集成方法与透明性在公共卫生决策中的协同价值。未来应探索更优插补策略、神经网络等算法及更新数据以进一步提升预测能力，支持全球健康政策制定。
原文摘要 · Abstract (English)
Life expectancy is a fundamental indicator of population health and socio-economic well-being, yet accurately forecasting it remains challenging due to the interplay of demographic, environmental, and healthcare factors. This study evaluates three machine learning models -- Linear Regression (LR), Regression Decision Tree (RDT), and Random Forest (RF), using a real-world dataset drawn from World Health Organization (WHO) and United Nations (UN) sources. After extensive preprocessing to address missing values and inconsistencies, each model's performance was assessed with $R^2$, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Results show that RF achieves the highest predictive accuracy ($R^2 = 0.9423$), significantly outperforming LR and RDT. Interpretability was prioritized through p-values for LR and feature importance metrics for the tree-based models, revealing immunization rates (diphtheria, measles) and demographic attributes (HIV/AIDS, adult mortality) as critical drivers of life-expectancy predictions. These insights underscore the synergy between ensemble methods and transparency in addressing public-health challenges. Future research should explore advanced imputation strategies, alternative algorithms (e.g., neural networks), and updated data to further refine predictive accuracy and support evidence-based policymaking in global health contexts.
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