用社会因素预测认知衰退，识别关键风险因子。
Interpretable Machine Learning for Cognitive Aging: Handling Missing Data and Uncovering Social Determinant
- 分类型处理缺失数据，结合奇异值分解与XGBoost建模
- 模型准确率超越基准，识别出地板材质等关键社会因素
- 适合关注健康公平与早期阿尔茨海默病预警的研究者
早期发现阿尔茨海默病至关重要，因其神经退行性变化不可逆，且病理与社会行为风险因素在确诊前数年已累积。本研究基于美国国立老龄化研究所支持的PREPARE挑战赛第二阶段数据，利用2003年和2012年墨西哥健康与老龄化研究（MHAS）中的全国代表性样本，预测认知表现。目标为2016年和2021年波次中七个领域（定向、记忆、注意力、语言、构图能力、执行功能）的综合认知评分。预测变量涵盖人口学、社会经济、健康、生活方式、心理社会及医疗可及性因素。采用基于奇异值分解（SVD）的缺失值填补流程，分别处理连续与分类变量，利用潜在特征相关性恢复缺失信息，兼顾可靠性与可扩展性。经多方法比较后，选用XGBoost实现最优预测性能。结果表明，该框架优于现有方法及挑战赛排行榜，具备高准确性、鲁棒性与可解释性。基于SHAP的后处理分析揭示了主要社会决定因素及其年龄特异性模式。显著发现：地板材质是强预测因子，反映社会经济与环境不平等；其他重要因子包括年龄、社会经济地位、生活方式、社交互动、睡眠、压力与体重指数，凸显认知老化多因素特性，证明可解释的数据驱动建模对社会决定因素研究的价值。
原文摘要 · Abstract (English)
Early detection of Alzheimer's disease (AD) is crucial because its neurodegenerative effects are irreversible, and neuropathologic and social-behavioral risk factors accumulate years before diagnosis. Identifying higher-risk individuals earlier enables prevention, timely care, and equitable resource allocation. We predict cognitive performance from social determinants of health (SDOH) using the NIH NIA-supported PREPARE Challenge Phase 2 dataset derived from the nationally representative Mex-Cog cohort of the 2003 and 2012 Mexican Health and Aging Study (MHAS). Data: The target is a validated composite cognitive score across seven domains-orientation, memory, attention, language, constructional praxis, and executive function-derived from the 2016 and 2021 MHAS waves. Predictors span demographic, socioeconomic, health, lifestyle, psychosocial, and healthcare access factors. Methodology: Missingness was addressed with a singular value decomposition (SVD)-based imputation pipeline treating continuous and categorical variables separately. This approach leverages latent feature correlations to recover missing values while balancing reliability and scalability. After evaluating multiple methods, XGBoost was chosen for its superior predictive performance. Results and Discussion: The framework outperformed existing methods and the data challenge leaderboard, demonstrating high accuracy, robustness, and interpretability. SHAP-based post hoc analysis identified top contributing SDOH factors and age-specific feature patterns. Notably, flooring material emerged as a strong predictor, reflecting socioeconomic and environmental disparities. Other influential factors, age, SES, lifestyle, social interaction, sleep, stress, and BMI, underscore the multifactorial nature of cognitive aging and the value of interpretable, data-driven SDOH modeling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。