Zhao Ming, Li Jie, Xiang Liuqing, Zhang Zu-Hai, Peng Sheng-Lung
School of Computer Science, Yangtze University, Jingzhou, China.
Department of Ophthalmology, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Front Aging Neurosci. 2022 Sep 7;14:984894. doi: 10.3389/fnagi.2022.984894. eCollection 2022.
As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods.
随着人口老龄化给家庭和社会带来严峻挑战，痴呆症问题也日益受到关注。痴呆症检测通常需要一系列复杂的测试和冗长的问卷，耗时较长。为了解决这一问题，本文针对问卷调查的诊断方法展开研究，希望通过机器学习方法建立诊断模型，帮助医生进行诊断，并运用特征选择方法挑选重要问题，以减少问卷中的问题数量，从而降低医疗成本和时间成本。本文以临床痴呆评定量表（CDR）作为数据源，运用多种方法进行建模和特征选择，以便将数据集中的相似属性合并，减少类别数量，最后使用混淆矩阵来评判效果。实验结果表明，采用装袋法建立的模型效果最佳，准确率可达真实诊断率的80%；在特征选择方面，主成分分析（PCA）与其他方法相比效果最佳。
