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.

翻译：卷积神经网络（CNNs）在处理同质数据（如图像）或连续数据间存在关联（如时间序列数据）时已被证明是有效的。尽管CNNs在处理表格数据方面并不知名，但我们证明其可应用于纵向数据，其中个体的信息在一段时期内被记录，因此数据间存在关联。本研究基于每两年进行一次的英国老龄化纵向调查（ELSA）。我们使用一维卷积神经网络（1D-CNNs），结合社会人口学特征、疾病状况、行动能力障碍、日常生活活动能力（ADLs）、工具性日常生活活动能力（IADLs）以及生活方式因素来预测死亡率。由于数据集高度不平衡，我们尝试了不同的过采样与欠采样方法，发现对小类别进行过采样能改善结果。我们还尝试了不同的激活函数进行模型测试。结果表明，swish非线性函数的性能优于其他函数。