用一维卷积网络预测英国中老年人死亡风险
Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach
- 将1D-CNN用于纵向健康数据，捕捉个体随时间变化的特征
- 在高度不平衡数据上，过采样小样本类别提升预测效果
- Swish激活函数表现最优，适合医疗风险预测任务
卷积神经网络（CNN）在图像或具有连续关系的时间序列数据中表现优异。尽管不常用于表格数据，本文展示其可用于纵向数据——即个体信息在多年间被重复记录的情况。研究基于每两年一次的英格兰老龄化纵向调查（ELSA），利用一维卷积神经网络（1D-CNN）预测死亡率，输入包括社会人口学特征、疾病史、行动障碍、日常生活能力（ADLs）、工具性日常生活能力（IADLs）及生活方式因素。由于数据高度不平衡，尝试了多种过采样与欠采样方法，发现增加少数类样本可显著提升模型性能。同时对比不同激活函数，结果表明Swish非线性函数优于其他选择。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) are proven to be effective when data are homogeneous such as images, or when there is a relationship between consecutive data such as time series data. Although CNNs are not famous for tabular data, we show that we can use them in longitudinal data, where individuals' information is recorded over a period and therefore there is a relationship between them. This study considers the English Longitudinal Study of Ageing (ELSA) survey, conducted every two years. We use one-dimensional convolutional neural networks (1D-CNNs) to forecast mortality using socio-demographics, diseases, mobility impairment, Activities of Daily Living (ADLs), Instrumental Activities of Daily Living (IADLs), and lifestyle factors. As our dataset is highly imbalanced, we try different over and undersampling methods and find that over-representing the small class improves the results. We also try our model with different activation functions. Our results show that swish nonlinearity outperforms other functions.
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