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.

研究使用机器学习中的生存分析方法，基于英国长寿研究队列ELSA数据，构建了两种基于随机森林和弹性网络算法的生存机器学习模型，并证明了这些模型相较于传统的Cox比例风险模型具有更优越的预测性能和稳定性。

使用生存机器学习和统计方法预测痴呆风险：以英国衰老长期研究队列为例的结果