Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach
This work addresses mortality prediction for aging populations, but it is incremental as it applies an existing CNN method to a new dataset with minor tweaks.
The study tackled mortality forecasting for middle-aged and older adults in England using a 1D-CNN on longitudinal data from the ELSA survey, achieving improved results with swish activation and oversampling methods.
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.