Predicting Risk of Dementia with Survival Machine Learning and
  Statistical Methods: Results on the English Longitudinal Study of Ageing
  Cohort
Daniel Stamate Henry Musto Olesya Ajnakina Daniel Stahl
Machine learning models that aim to predict dementia onset usually follow the
classification methodology ignoring the time until an event happens. This study
presents an alternative, using survival analysis within the context of machine
learning techniques. Two survival method extensions based on machine learning
algorithms of Random Forest and Elastic Net are applied to train, optimise, and
validate predictive models based on the English Longitudinal Study of Ageing
ELSA cohort. The two survival machine learning models are compared with the
conventional statistical Cox proportional hazard model, proving their superior
predictive capability and stability on the ELSA data, as demonstrated by
computationally intensive procedures such as nested cross-validation and Monte
Carlo validation. This study is the first to apply survival machine learning to
the ELSA data, and demonstrates in this case the superiority of AI based
predictive modelling approaches over the widely employed Cox statistical
approach in survival analysis. Implications, methodological considerations, and
future research directions are discussed.