Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.

中文翻译：

---

机器学习用于痴呆症的初步诊断


客观的。在痴呆症的早期阶段，可靠的诊断仍然是一个具有挑战性的问题。我们的目的是开发和验证一种基于机器学习的新方法，以帮助使用基于信息的问卷调查对正常、轻度认知障碍（MCI）、极轻度痴呆（VMD）和痴呆进行初步诊断。方法。我们招募了 5,272 名个人，他们填写了包含 37 项的调查问卷。为了选择最重要的特征，测试了三种不同的特征选择技术。然后，使用顶级特征结合六种分类算法来开发诊断模型。结果。信息增益是三种特征选择方法中最有效的。朴素贝叶斯算法在六种分类模型中表现最好（准确率 = 0.81、精度 = 0.82、召回率 = 0.81 和 F 测量 = 0.81）。结论。本文提出的诊断模型为临床医生诊断早期痴呆症提供了有力的工具。