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Machine learning models that aim to predict dementia onset usually follow theclassification methodology ignoring the time until an event happens. This studypresents an alternative, using survival analysis within the context of machinelearning techniques. Two survival method extensions based on machine learningalgorithms of Random Forest and Elastic Net are applied to train, optimise, andvalidate predictive models based on the English Longitudinal Study of AgeingELSA cohort. The two survival machine learning models are compared with theconventional statistical Cox proportional hazard model, proving their superiorpredictive capability and stability on the ELSA data, as demonstrated bycomputationally intensive procedures such as nested cross-validation and MonteCarlo validation. This study is the first to apply survival machine learning tothe ELSA data, and demonstrates in this case the superiority of AI basedpredictive modelling approaches over the widely employed Cox statisticalapproach in survival analysis. Implications, methodological considerations, andfuture research directions are discussed.

Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort