Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, Korea.
Int J Environ Res Public Health. 2021 Mar 4;18(5):2551. doi: 10.3390/ijerph18052551.
In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson's disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.
在这项研究中，我们使用标准化神经心理学测试的均方误差 (MSE) 来衡量支持向量机回归 (SVR) 的收敛速度，以确定帕金森病痴呆 (PDD) 的严重程度，并呈现基线数据，以便开发一种预测 PDD 严重程度的模型。我们分析了 328 名 60 岁或以上的 PDD 患者。为了确定具有最佳预测能力的 SVR，我们比较了八种 SVR 模型的分类性能（收敛率）（Eps-SVR 和 Nu-SVR，具有四种核函数（径向基函数 (RBF)、线性算法、多项式算法和 sigmoid））。在这八个模型中，Nu-SVR-RBF 的 MSE 最低（0.078），收敛率最高，而 Eps-SVR-sigmoid 的 MSE 为 0.110，收敛率最低。这项研究的结果表明，这种方法可以通过综合检查轴性非典型特征、韩国工具性日常生活活动 (K-IADL)、快速眼动睡眠行为障碍 (RBD) 的变化等来评估痴呆的严重程度，以便对独居老人或居住在医疗脆弱地区的 PDD 患者进行最佳干预和护理。
