# Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach

arXiv: https://arxiv.org/abs/2411.00317 · HTML: https://mlsanity.com/paper/2411.00317/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort](https://mlsanity.com/paper/2306.10330/) (2023)

## Abstract (arXiv)

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.