Department of Computer Science, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden.
Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden.
Comput Biol Med. 2024 Mar;171:108126. doi: 10.1016/j.compbiomed.2024.108126. Epub 2024 Feb 9.
The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia.
This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care - Blekinge (n=4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors.
Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms.
There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
老年人最常见的退行性疾病是痴呆症，可以通过多种指标进行预测，并且其进展可以减缓。痴呆症风险增加的一个指标是睡眠障碍。本研究旨在检验机器学习是否可以预测痴呆症，以及哪些睡眠障碍因素会影响痴呆症。
本研究使用了五种机器学习算法（梯度提升、逻辑回归、高斯朴素贝叶斯、随机森林和支持向量机）和来自瑞典国家老龄化和护理研究-布莱金厄（n=4175）的瑞典老年人口的数据。每个算法都使用 10 折分层交叉验证来获得结果，结果包括检查准确性的 Brier 得分和检查影响痴呆症的因素的特征重要性。这些算法使用了 16 个与个人和睡眠障碍因素相关的特征。
逻辑回归发现痴呆症与睡眠障碍之间存在关联。然而，对于研究中的特征来说，这种关联是微弱的。梯度提升是最准确的算法，准确率为 92.9%，f1 得分为 0.926，ROC AUC 为 0.974，Brier 得分为 0.056。在每个机器学习算法中，显著的因素都不同。如果一个人白天睡眠时间超过两小时，那么他们的性别、教育程度、年龄、夜间醒来以及是否打鼾是所有算法中特征重要性始终最高的变量。
睡眠障碍与痴呆症之间存在关联，机器学习算法可以预测这种关联。此外，痴呆症的风险因素在不同的算法中有所不同，但睡眠障碍可以预测痴呆症。
