Dementia in older age is a major health concern with the increase in the aging population. Preventive measures to prevent or delay dementia symptoms are of utmost importance. In this study, a large and wide variety of factors from multiple domains were investigated using a large nationally representative sample of older people from the English Longitudinal Study of Ageing. Seven machine learning algorithms were implemented to build predictive models for performance comparison. A simple model ensemble approach was used to combine the prediction results of individual base models to further improve predictive power. A series of important factors in each domain area were identified. The findings from this study provide new evidence on factors that are associated with the dementia in later life. This information will help our understanding of potential risk factors for dementia and identify warning signs of the early stages of dementia. Longitudinal research is required to establish which factors may be causative and which factors may be a consequence of dementia.

中文翻译：

---

数据挖掘方法在痴呆症预测中的应用：来自英语年龄纵向研究的证据

随着人口老龄化的增加，老年痴呆症是一个主要的健康问题。预防或预防痴呆症状的预防措施至关重要。在这项研究中，使用来自英国纵向老龄化研究的大量具有全国代表性的老年人样本，研究了来自多个领域的多种多样的因素。实施了七种机器学习算法，以构建用于性能比较的预测模型。使用简单的模型集成方法来组合各个基本模型的预测结果，以进一步提高预测能力。确定了每个领域中的一系列重要因素。这项研究的发现为以后的生命中与痴呆症相关的因素提供了新的证据。这些信息将帮助我们了解痴呆症的潜在危险因素，并确定痴呆症早期阶段的警告信号。需要进行纵向研究以确定哪些因素可能是致病因素以及哪些因素可能是痴呆症的结果。